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7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | ba2xGOLBIs | official_review | 1,731,036,881,417 | 7if3xZkBbG | [
"everyone"
] | [
"~Anton_Johansson1"
] | title: Well written proposal
review: Overall it is av very interesting topic that has the potential to be truly helpful if successful. You have an innovative use of synthetic data to reduce dependence on extensive human speech datasets. However, your proposal lacks some technical details on model architecture, particularly the voice conversion block and embeddings. To strengthen your proposal, it could include a detailed evaluation strategy, perhaps using metrics like mean opinion scores, and discuss challenges in synthetic data quality. I understand that the page limit make it challenging to include all these details, but these points could be useful to consider for the final project. Very good job overall.
rating: 9
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | XCQzKVokh4 | official_review | 1,731,424,255,147 | 7if3xZkBbG | [
"everyone"
] | [
"~Grace_Xin-Yue_Yi1"
] | title: Review
review: This proposal defines the key challenges of TTS models, outlining the benefits of using synthetic data to address issues like privacy and the high costs of traditional models. Related works analyze the limitations of existing TTS models such as WaveNet, Tacotron, FastSpeech, and zero-shot synthesis models like VALL-E and OpenVoice. The discussion is thorough, addressing each model's strengths and weaknesses, particularly around computational efficiency and generalization capabilities. The proposed methodology is innovative but could benefit from including more technical details such as the data generation process, how the methodology overcomes the limitations of existing models mentioned in the related work section, and specifying evaluation metrics.
rating: 8
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | Ulu5xbq9vm | official_review | 1,731,340,403,489 | 7if3xZkBbG | [
"everyone"
] | [
"~Gausse_Mael_DONGMO_KENFACK1"
] | title: Ambitious goal, perfectible path
review: The paper proposes a method for voice cloning in TTS systems. The approach aims to reduce the need for extensive human speech datasets, thereby lowering costs and protecting user privacy. The model would take an audio file and a brief voice sample to generate speech that mimics the target speaker’s voice.
strength: Since data is central to most current ML methods, a generalizable approach enabling the use of synthetic data would be highly beneficial for the field.
weakness: The paper would benefit from additional clarity on the model architecture and audio data representation.
rating: 8
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | TZQcNl74Pm | official_review | 1,730,883,330,595 | 7if3xZkBbG | [
"everyone"
] | [
"~Chua_Shei_Pern1"
] | title: Clear approach
review: The proposal had a very strong and solid proposed method, which is commendable. The background and methodology provides good context to understand the project.
rating: 10
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | TUF8VQOXef | official_review | 1,731,415,608,848 | 7if3xZkBbG | [
"everyone"
] | [
"~Kittaphot_Saengprachathanarak1"
] | title: Review of "Mimicking Humanity"
review: This proposal outlines a novel approach to voice cloning in text-to-speech (TTS) systems, focusing on using synthetic data to reduce reliance on large, human-generated speech datasets. The authors aim to develop a model that can take input audio and modify it to match the characteristics of a target speaker, using only a short voice sample. By leveraging synthetic data for pre-training, the model can generalize across various speaker profiles without extensive fine-tuning, thus addressing challenges related to data scarcity and privacy concerns. The approach is promising, particularly in its potential to create smaller, more efficient models for local inference. However, further validation through experiments and comparison with existing methods like OpenVoice and VALL-E would strengthen the proposal. Overall, the methodology is well-structured and offers significant contributions to voice cloning in TTS systems, with potential for broader applications in privacy-preserving speech synthesis.
rating: 10
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | M6bDzMeuiz | official_review | 1,731,305,764,696 | 7if3xZkBbG | [
"everyone"
] | [
"~Rim_El_Filali1"
] | title: Promising Voice Cloning with Synthetic Data but Needs Clearer Evaluation Metrics
review: This proposal presents a voice cloning model designed to enable personalized TTS experiences while addressing the common challenges of high data acquisition costs, privacy concerns, and linguistic diversity in traditional TTS. The model uses synthetic training data to avoid reliance on human speech recordings, aiming for a generalized voice conversion model with minimal real-world data.
Pros:
- Tackling privacy in voice generation by allowing users to clone voices locally, without third-party data rights, is a timely and impactful goal.
- The goal of using short voice samples to perform voice cloning across a variety of speakers with minimal fine-tuning could make the model highly adaptable to new users.
Cons:
- Limited details are provided regarding the model’s computational requirements and how it will ensure high-quality voice cloning in real-time on local devices.
- The proposal lacks specifics on how the model’s success will be quantitatively evaluated. Performance metrics would be helpful to understand its potential effectiveness.
rating: 9
confidence: 4 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | LpKXgQOfjn | official_review | 1,730,971,667,605 | 7if3xZkBbG | [
"everyone"
] | [
"~Huajun_Bai1"
] | title: Synthetic Data-Driven Voice Cloning in TTS: Privacy, Innovation, and Challenges
review: Strengths
1. Innovative Use of Synthetic Data: The proposal to train a voice cloning model for TTS systems using synthetic data is a forward-thinking approach that addresses the challenges of data acquisition, including cost, privacy, and linguistic diversity. This method has the potential to democratize access to high-quality TTS systems.
2. Privacy-Preserving Local Inference: The focus on creating a model that allows for local inference without the need to share user data with third parties is a significant strength. It addresses the growing concern over data privacy and provides a more secure alternative to current commercial voice generation platforms.
3. Generalizability and Cost Reduction: The proposal's aim to develop a model that can generalize well without reliance on human-produced training data is commendable. If successful, this approach could reduce the cost of acquiring training data for other ML models, making it a valuable contribution to the field of machine learning.
Weaknesses
1. Technical Details on Synthetic Data Generation: While the proposal highlights the use of synthetic data, it lacks specific details on how this synthetic data will be generated and validated for quality. Ensuring that synthetic data accurately mimics the characteristics of real human speech is crucial for the success of the model.
2. Evaluation Metrics and Benchmarks: The proposal does not discuss how the performance of the voice cloning model will be evaluated against existing TTS models or industry standards. Defining clear evaluation metrics and benchmarks is essential for demonstrating the effectiveness of the proposed system.
rating: 7
confidence: 3 |
7if3xZkBbG | [Proposal-ML] Mimicking Humanity: A synthetic data-based approach to voice cloning in Text to Speech Systems | [
"Michael Hua Wang",
"Aleksandr Algazinov",
"Joydeep Chandra"
] | Acquisition of training data poses significant challenges in many areas of machine learning. Data paucity has a negative impact on model accuracy and bias. One potential way to obtain more training data is by synthesizing it using some external tools. This project presents a voice cloning technique using synthetic data for text-to-speech systems, to address issues with data accessibility and privacy in conventional text-to-speech models. By utilizing artificial synthetic voice data, the model is created to mimic voice features such as pitch, tone, and timbre, allowing for the creation of speech that mimics a specific speaker while also keeping the original linguistic content intact. This method aims to improve model generalization across different speaker profiles and languages by not requiring large human speech datasets, allowing for local inference without needing proprietary data. If successful, this will demonstrate that synthetic data can be used to train AI systems if the task it is applied to is chosen correctly. | [
"Voice Cloning",
"Speaker Generalization",
"Text-To-Speech",
"Synthetic Data",
"Privacy"
] | https://openreview.net/pdf?id=7if3xZkBbG | BslU46CKjE | official_review | 1,731,236,194,031 | 7if3xZkBbG | [
"everyone"
] | [
"~Diego_Cerretti1"
] | title: Clear, relevant and well-written
review: The authors propose a voice cloning model for text-to-speech systems that uses synthetic training data to enable personalized, privacy-focused speech synthesis. The proposed method is clearly motivated and aligns with the current trends in TTS research. The objective and the technical approach are well-defined.
rating: 10
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | v5vic9jvCh | official_review | 1,731,079,214,166 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Joydeep_Chandra2"
] | title: Comprehensive new approach using RC in IoT and Edge domain but requires performance clarity on real life implementation
review: The focus on overcoming latency issues by using Reservoir Computing (RC) is timely and aligns well with the industry shift towards edge-based AI. The proposal provides a good overview of current state-of-the-art ASR models (e.g., Whisper and Wav2Vec 2.0) and optimization strategies. It shows a comprehensive understanding of the existing landscape and highlights the limitations of transformer models in resource-constrained scenarios. Although the proposal lists evaluation metrics like Word Error Rate (WER), Character Error Rate (CER), and inference time, it does not provide a clear plan on baseline comparisons or target benchmarks. More specific performance goals would help set expectations for the next phase.
rating: 10
confidence: 5 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | qmX60B8b2s | official_review | 1,731,344,383,756 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Michael_Hua_Wang1"
] | title: Review
review: Model size and computational requirements remain a perennial pain point and pose a major obstacle to broader adoption. The authors propose to apply reservoir computing (RC) to automatic speech recognition systems, seeking to reduce the resource requirements for a typical system while sidestepping the network-related latency induced by cloud computing-based approaches.
The proposal describes a plausible and novel application of this approach, and if successful, it can prove the viability of an approach to making models more accessible to consumers.
rating: 9
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | qG8czVPUox | official_review | 1,731,325,947,198 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Hector_Rodriguez_Rodriguez1"
] | title: Review "Reservoir Computing for Edge-based Automatic Speech Recognition"
review: The authors make an excellent case for the use of Reservoir Computing for Automatic Speech Recognition (ASR) in memory-constrained devices.
- The introduction and background sections justify the need for lightweight ASR systems on edge devices.
- The related work highlights some approaches to optimize the resource utilization on ASR systems.
- The proposal section includes the datasets and other implementations that could be compared with the current RC-based approach. This section could benefit from a more detailed explanation on the RC architecture that would be implemented, as well as the target resource utilization and the desired inference time.
Overall, the proosal is well written and complies with all the requirements.
rating: 10
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | bH2HPleeg8 | official_review | 1,731,427,158,517 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Chendong_Xiang1"
] | title: review
review: This paper explores using Reservoir Computing (RC) for Automatic Speech Recognition (ASR) on edge devices, addressing the challenges posed by traditional ASR models that require high memory and computational power, such as those based on transformers. Unlike these models, RC has a fixed, untrained internal reservoir layer, allowing efficient processing of time-series data with minimal resource requirements. This makes it well-suited for memory-limited edge devices.
The study tests an RC-based ASR model on datasets like LibriSpeech and Common Voice, comparing it to smaller transformer models (e.g., Whisper Small) in terms of Word Error Rate (WER), Character Error Rate (CER), and inference time. By fine-tuning RC parameters, such as reservoir size and connectivity, the authors aim to enhance accuracy without compromising efficiency.
rating: 8
confidence: 2 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | TV1YKSLwix | official_review | 1,731,315,099,916 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Ziyad_Fawzy1"
] | title: Reservoir Computing for Edge-based Automatic Speech Recognition
review: This paper explores Reservoir Computing (RC) as a solution to the limitations of current Automatic Speech Recognition (ASR) models on edge devices, which often struggle with high memory demands and latency when using transformer-based models. By leveraging RC’s low memory footprint and ability to capture complex temporal features, the authors propose an RC-based ASR model for edge applications, aiming to achieve high accuracy and low latency.
The authors clearly and rigorously explained the problem and their proposed method. Their work is practical and has potential in industrial applications.
rating: 10
confidence: 5 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | RyiI8UzoNx | official_review | 1,731,407,077,099 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Eddy_Yue1"
] | title: Strong Proposal on Edge-based ASR Using Reservoir Computing
review: This project offers a compelling alternative to traditional ASR models for edge deployment. Addressing accuracy constraints may help ensure practical applications across various edge devices.
Related work section is thoroughly researched, overall detail in proposal is convincing.
rating: 10
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | JjYI5HOLow | official_review | 1,731,412,893,582 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Justinas_Jučas3"
] | title: Extremely Clear and Well-Structured proposal
review: In general, the proposal is extremely concrete and clear to understand. The significance of the work is also important and described in the proposition. All of the basic requirements are fulfilled. In addition, I would say that the project also seems feasible to be implemented within limited time constraints, which makes it realistic.
## Advantages
1. Very clear and well-structured report
2. Satisfies all of the requirements
3. The proposal is ambitious, yet seems feasible to be implemented
## Disadvantages
1. Perhaps the Related Work/Proposal sections lack some discussion on the potential limits of the RC, as these obviously exist. However, this is the only one I could think ok.
rating: 10
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | GX45k2Sg6n | official_review | 1,731,390,071,704 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Zhuofan_Sun1"
] | title: Review
review: Strengths:
Relevance: The proposal addresses a crucial challenge in ASR, i.e., achieving high accuracy with low latency and resource consumption on edge devices. Reservoir Computing (RC) offers a promising solution due to its inherent properties.
Clarity: The proposal is well-structured and clearly explains the motivation, background, related work, and the proposed approach. The problem statement is clear, and the objectives are well-defined.
Feasibility: The proposal outlines a feasible plan with specific datasets, evaluation metrics, and comparison methods. The team also demonstrates an understanding of potential challenges and suggests approaches to address them.
Technical Soundness: The proposal demonstrates a solid understanding of RC and its applicability to ASR. The team acknowledges the importance of optimizing RC parameters for improved performance.
Areas for Improvement:
Specificity: The proposal could benefit from more specific details on the proposed RC architecture and optimization strategies. For example, what type of non-linear activation function will be used? How will the reservoir size and connectivity be determined?
Evaluation Plan: While the proposal mentions using WER, CER, and frame-wise accuracy, it would be beneficial to elaborate on the specific evaluation protocols, such as the training and testing splits, and the metrics for comparing memory usage and inference time.
Comparison Baselines: The proposal mentions comparing the RC model with Whisper Small and Wav2Vec 2.0. It would be helpful to justify the selection of these baselines and discuss any potential limitations.
Potential Challenges: The proposal acknowledges potential challenges but could provide more detailed discussion on how these challenges will be addressed. For example, how will the team ensure the robustness of the RC model across different accents and languages?
Overall, the proposal presents a compelling case for exploring RC for edge-based ASR. Addressing the areas for improvement will further strengthen the proposal and increase its chances of success.
Additional Suggestions:
Explore Different RC Variants: The proposal could consider exploring different types of RC, such as Echo State Networks (ESNs) or Liquid State Machines (LSMs), and compare their performance.
Investigate Transfer Learning: The proposal could investigate using transfer learning techniques to fine-tune pre-trained RC models on the target dataset, potentially improving accuracy and reducing training time.
Consider Edge-specific Hardware Acceleration: The proposal could explore utilizing specialized hardware accelerators for RC, such as FPGAs or neuromorphic chips, to further reduce latency and power consumption.
I believe this proposal has the potential to make significant contributions to the field of ASR and edge computing. I recommend further refining the details and addressing the areas for improvement to ensure a successful project.
rating: 10
confidence: 5 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | Bb5eObJVms | official_review | 1,730,882,459,662 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Aleksandr_Algazinov1"
] | title: Well-explained, convincing, and relevant
review: The proposal is well-written and easy to follow. The authors do research on a rather new ASR problem. Based on various references, the authors proposed to experiment with a modern architecture. The project presents possible steps to improve the model, as well as a convincing motivation for the study.
rating: 10
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | 4jV3jfRvLe | official_review | 1,731,321,245,263 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Shuangyue_Geng1"
] | title: Promising approach and well-designed structure
review: The proposal is novel, well-structured, and easy to understand. It presents a promising approach to improving edge-based Automatic Speech Recognition (ASR) by leveraging Reservoir Computing (RC) to reduce latency and memory usage compared to transformer-based models. It effectively highlights the need for low-resource ASR solutions and provides a solid overview of the current state-of-the-art. Moreover, it provides a comprehensive review of related work. Overall, this well-motivated research direction has the potential for significant impact.
rating: 10
confidence: 3 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | 3xN5HTWhmd | official_review | 1,731,046,762,648 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Bowen_Su1"
] | title: Comprehensive Proposal and Clear Methods
review: This proposal comprehensively reflects the author's thoughts and ideas. In response to the current problem of balancing efficiency and effectiveness in ASR, the author proposes using RC to improve the performance of edge ASR models.
The author provides a detailed introduction to the proposed method and further proposes evaluation criteria and detailed steps. Covered the requirements of the proposal.
rating: 9
confidence: 4 |
6lIF68Ooq4 | Reservoir Computing for Edge-based Automatic Speech Recognition | [
"Thomas Adler",
"Diego Cerretti",
"Nicolo Micheletti"
] | Automatic Speech Recognition (ASR) ensures seamless interaction between humans and LLM-powered AI. Current state-of-the-art ASR models are transformer-based neural networks that have a very high level of accuracy but come with the cost of high complexity, partly due to the attention mechanisms present in the model. The latest ASR model by Open AI, Whisper Large, has 1.55bn parameters (~2.9 GB) and is reported by users to require around 12 GB of VRAM to run. A model of this size has to be deployed on the cloud, which introduces network latency, slowing response times and degrading user experience. Indeed, edge devices cannot host a model of this size: the latest iPhone 15 Pro Max is estimated to have around 8 GB of RAM. Due to limited resources, current edge-based ASR models also struggle with accuracy. Reservoir Computing offers the potential for a new generation of edge-based ASR models with low latency and high accuracy. | [
"ASR",
"Reservoir Computing",
"Edge Computing",
"Neuromorphic Computing"
] | https://openreview.net/pdf?id=6lIF68Ooq4 | 2mBkBcUZcX | official_review | 1,731,316,655,945 | 6lIF68Ooq4 | [
"everyone"
] | [
"~Anqi_LI5"
] | title: review
review: this proposal presents a promising and well-motivated research direction. Addressing the weaknesses mentioned above, particularly by providing more details on the approach and expanding the evaluation scope, would further strengthen the proposal and increase its potential for success.
Pros:
Addresses a critical gap in the field of ASR.
Clearly articulates the problem statement and research objectives.
Acknowledges related work.
Potential for high impact on the field of ASR.
Cons:
Lack of specificity in the approach.
Limited scope of evaluation.
Potential for overfitting.
rating: 9
confidence: 3 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | tSJOhSvTqO | official_review | 1,731,329,160,212 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Yifan_Luo2"
] | title: A paper need to be improved
review: LLM's ability in Mathmatical reasoning is crucial for achieving AGI/AI4SCI. This paper introduced MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning.
I have some questions and suggestions.
Q1. What dataset do you want to use?
Q2. What model do you want to use?
Q3. Do you know about Qwen2.5-Math-Instruct-1.5B? It achieves 75.8 Zero-shot@1 Acc on MATH. It is so amazing (It has so small params!) and I don't know how much room there is for improvement in your model.
S1. The paper has already reached the page limit.
S2. Maybe you can give a clearer roadmap for your work.
S3. Some of the works mentioned in the paper are not included in the references.
rating: 7
confidence: 5 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | sMWpTdybdI | official_review | 1,731,044,832,133 | 61OPn1Y5u2 | [
"everyone"
] | [
"~王俊逸1"
] | title: Innovative Mathematical Reasoning Model with Rigorous Evaluation
review: The proposal for MathLLaMA presents a significant and innovative approach to enhancing AI capabilities in mathematical reasoning and problem-solving. It offers a clear vision, a structured plan, and a focus on rigorous evaluation, which are all strengths. However, the proposal's complexity, dependency on datasets, and limited scope to mathematical domains are areas that could be further addressed. Overall, MathLLaMA has the potential to make a substantial contribution to the field of AI and mathematical applications.
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | pi8B4GFIUp | official_review | 1,731,252,006,916 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Chentian_wei1"
] | title: The proposal offers a comprehensive system for LLMs in solving math problems but may benefit from a more detailed methods section to avoid appearing incomplete.
review: The paper provides a very systematic introduction and summary of LLMs solving mathematical problems, with a clear roadmap in the method design. Although it exceeds the page limit, the content is very rich as a proposal. However, I believe there might be a need for further description in the methods section; otherwise, the proposal might seem to have a strong start but a weak finish.
rating: 7
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | kQVCnWofS0 | official_review | 1,731,320,414,601 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Jin_Zhu_Xu1"
] | title: Fully explained but not structurized
review: The proposal includes a clear understanding of the idea of the problem topic. Technical terms here are not thoroughly defined within the context. It would be better if it is structurize with clearer explanation to point out how MathLLaMA outperforms existing models at a glance.
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | ZivYPIm3oQ | official_review | 1,731,331,298,706 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Zheng_Jiang2"
] | title: Impactful Research but Disorganized Structure
review: The paper introduces MathLLaMA, a specialized language model fine-tuned for mathematical reasoning and problem-solving. The use of curriculum learning and prompt engineering is well-justified and aligns with best practices in fine-tuning LLMs for specialized domains.
However, I have several questions and suggestions regarding this paper: (1) The length of the paper does not comply with the proposal's requirements, and the organization of the content across different sections is chaotic. (2) I am not well-versed in this field. You mentioned 'first generating intermediate thought processes, then producing the corresponding Python code'—are examples of answering questions with Python code widely adopted in this area? What are the advantages and disadvantages of this approach compared to directly generating answers? (3) COT has achieved excellent results; why don't you just combine it with producing Python code?
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | YKoUBHaBZo | official_review | 1,731,251,565,644 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Shuangyue_Geng1"
] | title: Promising work with greater potential through clearer structure
review: This proposal presents an innovative approach to a Mathematical Reasoning Model, effectively addressing limitations of current LLMs in math tasks. However, the structure lacks clarity, blending completed work with proposed methods, which makes it difficult to distinguish between the two. Additionally, exceeding the two-page limit, with some repetition and disjointed sections, affects readability. A clearer, more concise structure would enhance the proposal’s impact.
rating: 7
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | Sy79OtZbCt | official_review | 1,731,237,623,053 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Diego_Cerretti1"
] | title: Relevant but unclear
review: The authors propose a specialised large language model for mathematical reasoning. While the proposal addresses a relevant problem, it could benefit from further refinement. The paper isn't well-structured and it exceeds the 2-pages limit. A clearer and more concise structure would allow the reader to follow the proposal more easily.
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | KT5yEXekRg | official_review | 1,730,991,441,587 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Anton_Johansson1"
] | title: A project with good potential when refined
review: The paper is well-organized and the authors seem knowledgeable. However, it exceeds the 2-page limit, and there is some repetition, particularly between the abstract and introduction, as well as duplicate explanations of Curriculum Learning. Additionally, the first half is written in the past tense, which implies the project has already been completed, as suggested by phrases like "Our results indicate that...". Overall, this is a very interesting topic, and a model like MathLLaMA has significant potential utility.
rating: 7
confidence: 3 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | GMjAzdOKbT | official_review | 1,731,421,578,148 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Fei_Long3"
] | title: Review on MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving
review: **Strengths**:
1. **Technical Approach:** The use of curriculum learning and prompt engineering for fine-tuning is a strong methodological choice that aligns with the complexity of mathematical problem-solving.
2. **Comprehensive Materials**: The proposal is well-equipped with a thorough presentation of benchmarks, datasets and methodologies, which is essential for evaluating the performance of the proposed MathLLaMA model.
**Weaknesses**:
1. **Lack of Supporting Data**: The proposal states, "Our results indicate that MathLLaMA outperforms existing language models across several metrics, demonstrating its effectiveness in solving mathematical tasks and generating accurate solutions." in Section 1.1. To substantiate this claim, the authors should provide corresponding data results that validate the superiority of MathLLaMA.
2. **Exceeding Page Limit**: The proposal exceeds the two-page limit, which is a critical requirement for proposal submissions. In addition, the proposal could be organized in a better way.
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | 9gIeYGCc91 | official_review | 1,730,817,974,576 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Thomas_Adler2"
] | title: An innovative idea but lacking clarity
review: The reasoning behind the project is clear and the pain point of current LLMs when dealing with math problems is well explained. The general idea of improving mathematical reasoning seems innovative and interesting. It seems that the authors are knowledgeable about the current research area. However, the writing of the proposal is confusing:
1. Half of the proposal is in the past tense, as if this was a submitted paper.
2. There is a lot of repetition and different sections seem very disjointed.
3. Because of this the page limit has been doubled!
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | 98J8KQNwZS | official_review | 1,731,410,213,292 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Ruowen_Zhao1"
] | title: Review on MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving
review: **Summary**
Enabling large language models (LLMs) to handle mathematical tasks remains challenging due to the unique nature of mathematical language. MathLLaMA aims to address the limitations through a targeted fine-tuning approach involving curriculum learning, data augmentation, and prompt engineering. Experimental results show that MathLLaMA excels in problem-solving accuracy, symbolic reasoning, and equation generation.
**Strength**
The proposal provides a thorough explanation of problem definition, and key challenges and technical routes, offering readers a clear understanding of the work's focus and scope.
**Weakness**
The proposal goes beyond the two-page limit, which may not meet submission guidelines. It is also suggested that the authors should ensure the correctness of the generated math dataset from GPT-4.
rating: 8
confidence: 4 |
61OPn1Y5u2 | 【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving | [
"Yu Zhang",
"Changsong Lei"
] | As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts. | [
"Machine learning",
"Large Language Model",
"Mathematics"
] | https://openreview.net/pdf?id=61OPn1Y5u2 | 7MYDEOunFL | official_review | 1,731,407,839,045 | 61OPn1Y5u2 | [
"everyone"
] | [
"~Kairong_Luo1"
] | title: Complete materials, Disorganized structures, Insufficient novelty
review: Strengths:
1. The proposal materials are complete, including the background discussion, related works review and methods explanation. The benchmarks, datasets are included;
2. The main idea of curriculum learning with CoT is intuitively reasonable.
Weakness:
1. The proposal is not well organized. For example, as a proposal, it is strange to pre-claim the contributions;
2. The related works review are not well-done. like https://arxiv.org/html/2410.21728v1 seems having done the same works;
3. The analysis seems not deep enough, like the reasons that previous works do not work. The materials are listed instead.
rating: 7
confidence: 5 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | uSDT5o5pdt | official_review | 1,730,887,281,243 | 5mE1shG2va | [
"everyone"
] | [
"~Guilherme_Félix_Diogo1"
] | title: Complete proposal, almost no flaws
review: This proposal introduces the large language models (LLMs) application for predicting bandwidth over different time periods in order to facilitate better resource management and improve the experience of users engaging in streaming media content. The approach is comprehensive and includes a benchmark for real-world bandwidth distribution as well as short and long-range predictions, and covers several industry scenarios from live bandwidth streaming to CDN based optimization. The proposed benchmark construction through tail importance sampling and application of TimeLLM, which has been fine tuned for bandwidth predictions, are highly practical and aim at addressing both the short term variations and the long term changes of the bandwidth data over the time. Could be a little more detailed on how it intends to assess the predictive performance across different contexts, particularly with respect to adjusting in real time.
rating: 9
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | r3oS3KJ0hv | official_review | 1,731,419,767,803 | 5mE1shG2va | [
"everyone"
] | [
"~Justinas_Jučas3"
] | title: Clear and Well-Structured Proposal with Clear Disadvantages
review: The proposal is written on a new algorithm of a well-established an relevant problem. It contains well-written, original and clear idea for solution, however, in my eyes, it is not clear why the proposed solution is not an overkill for the given problem. In addition, several key requirements are not satisfied.
## Advantages
1. A well-structured and easy-to-read proposal
2. The proposed algorithm is rather unique and clear
3. Most of the requirements are fulfilled
## Disadvantages
1. References to concrete datasets used are missing (this was a requirement!)
2. No specific details of how the performance is evaluated (what metrics can/will be used) are provided (this was a requirement!)
3. I do not see why a LLM is more beneficial than using for instance a simple NN. Why not use some regular time series estimation technique? Is LLM not an overkill for this task?
4. Lack of references. For example, many fact statements in the background section are not based on any sources, which is bad practice.
rating: 7
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | nAzadH8j3o | official_review | 1,731,044,143,203 | 5mE1shG2va | [
"everyone"
] | [
"~Bowen_Su1"
] | title: Good and Comprehensive Proposal
review: A comprehensive introduction was given to the benchmark construction involved in the task and the conceptual framework of the model. In addition, a detailed and comprehensive literature review has been conducted on this issue.Fully meets the requirements of the proposal, good work.
rating: 9
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | mWa3eVTpEU | official_review | 1,731,257,376,962 | 5mE1shG2va | [
"everyone"
] | [
"~Keyu_Shen1"
] | title: Clear and Well-organized Proposal
review: The background of proposed work is clearly stated in the proposal, and problem definition is effectively established. Main goals include benchmark constructing and TimeLLM-based framework developing, which has great potential of advancing cutting-edge AI tools in bandwidth prediction. However, the proposal would benefit from a more detailed discussion on computational resource management and response speed optimization, as the high computational demands of LLMs may pose challenges for real-time performance.
rating: 8
confidence: 3 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | lJ9vTOJZhm | official_review | 1,731,244,928,416 | 5mE1shG2va | [
"everyone"
] | [
"~Guanglei_He1"
] | title: The overall proposal is relatively clear, establishing an benchmark dataset is likely to be extremely challenging.
review: Overall, The entire proposal to be exceptionally well-written—concise and clear. It is currently very lucid, and it appears that substantial effort has been invested in describing related work.
After a comprehensive review, I have a few suggestions:
1. **Benchmark Dataset Establishment**
I understand that the most critical aspect of this work lies in establishing a benchmark dataset. Having this dataset as a foundation is essential to validate the effectiveness of subsequent methods.
As I am not deeply familiar with this field, I am uncertain about the difficulty involved in collecting this data. The proposal mentions collecting data from three different scenarios, and there are several key considerations:
- **Specific Application Data**: Is the data being collected from certain specific applications? The characteristics of network data streams from different applications are likely to be entirely different, which could result in fundamentally different models or methods. I believe that such application types should be explicitly specified in this proposal.
- **Scope of Data Collection**: What is the extent of the data collection? For example, considering WeChat, its data is extremely widespread, distributed across national nodes, and much of it can be processed locally. Comprehensive data collection would be a highly challenging task. If the data is not comprehensive, could this negatively impact the overall effectiveness of the application?
2. **Choice of Models in Related Work**
The Related Work section mentions that the industry predominantly uses linear model-based models due to their simplicity and efficiency. However, this proposal directly employs Large Language Models (LLMs). I personally feel that using LLMs in this context might be overly heavy. If the goal is merely to extract better features, I believe traditional models like CNNs and LSTMs could achieve an excellent balance between efficiency and effectiveness in this scenario. How has this been considered?
rating: 8
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | cuz3S37QJE | official_review | 1,731,392,679,641 | 5mE1shG2va | [
"everyone"
] | [
"~Chumeng_Jiang1"
] | title: Well-structured, but some details still need to be supplemented.
review: This proposal puts forward the idea of leveraging large language models (LLMs) to design a foundational bandwidth prediction algorithm applicable to multiple scenarios, such as long-term and short-term live streaming. To achieve this goal, the authors expect to first construct a benchmark using tail importance sampling, and then propose "TimeLLM" to finetune the LLM on this benchmark dataset.
Strengths:
- **Well-structured and well-planned:** The proposal has a clear logical structure, with a defined problem, methodology, and planned steps for implementation.
- **The problem is significant and ambitious:** The aim is to develop a foundational bandwidth prediction algorithm for diverse scenarios with varying characteristics.
Weaknesses:
- **Insufficient details on how LLMs will be applied, with unclear necessity for LLMs:** It’s still unclear how the authors plan to utilize LLMs. Is it intended to transform all features into natural language and input them into the LLM? If that’s the case, how would this approach enable "harnessing the capabilities of large language models to model the temporal dependencies of bandwidth"?
rating: 8
confidence: 3 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | YbvLNpUT2H | official_review | 1,731,054,446,609 | 5mE1shG2va | [
"everyone"
] | [
"~Zhen_Leng_Thai1"
] | title: Comprehensive Proposal regarding Bandwidth Prediction based on LLM
review: This paper presents an LLM-based bandwidth prediction model for diverse streaming scenarios, supported by a robust benchmark dataset. Definition and methodology are well-defined, leveraging TimeLLM for real-time accuracy. However, more citations in the background section and reasoning for choosing TimeLLM could be beneficial.
rating: 9
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | HIGg1Y4S6q | official_review | 1,731,424,195,699 | 5mE1shG2va | [
"everyone"
] | [
"~Kuanghao_Wang1"
] | title: Interesting application direction
review: This PROPOSAL explains well the significance of the study that it is necessary and valuable to use machine learning for bandwidth prediction. Also, this PROPOSAL gives the specific problem and the methodology of prediction with a more extensive literature research. A slight shortcoming is that this paper does not give an explanation as to why TimeLLM is used as a method for prediction. It might be better if this aspect can be reinforced.
rating: 9
confidence: 3 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | G2Ra4aAYAe | official_review | 1,731,326,586,407 | 5mE1shG2va | [
"everyone"
] | [
"~Yu_Zhang61"
] | title: Review of "A Large Language Model-based Bandwidth Prediction Algorithm"
review: This thesis proposal introduces a promising approach for improving bandwidth prediction in the streaming media industry by leveraging large language models (LLMs) and Transformer architectures. The use of LLMs for bandwidth prediction is innovative, as these models have demonstrated success in extracting complex features and managing long-term dependencies, which could significantly enhance the accuracy and responsiveness of bandwidth predictions in diverse streaming contexts. The proposal is well-motivated, addressing the practical implications of improved bandwidth prediction, such as enhanced user experience through better bitrate selection and lower CDN costs due to optimized bitrate scheduling. However, while the potential benefits are clear, the proposal would benefit from a more detailed discussion of the specific challenges and complexities of adapting LLMs to bandwidth prediction over other established time-series models. A more concrete outline of the proposed architecture and evaluation metrics, as well as consideration of computational costs associated with deploying LLMs in real-time prediction contexts, would enhance the proposal's feasibility and clarity.
- I hope the author will elaborate on the detailed role and specific implementation of vit in this framework.
rating: 8
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | 5hIgospUPE | official_review | 1,731,393,630,340 | 5mE1shG2va | [
"everyone"
] | [
"~Eddy_Yue1"
] | title: Good idea!
review: Overall, the project presents a promising solution to bandwidth prediction challenges using advanced modelling techniques. Using tail importance sampling to enhance dataset accuracy focusing on low-bandwidth scenarios aligns well with the project's objectives. A contingency plan could strengthen the model's adaptability.
rating: 9
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | 5PDXIgrbwv | official_review | 1,731,397,853,470 | 5mE1shG2va | [
"everyone"
] | [
"~ChenJian1"
] | title: Highly Innovative
review: The proposal presents a bandwidth prediction algorithm based on Large Language Models (LLMs) aimed at improving user experience and resource optimization in the streaming media industry. The core of the project is to develop a bandwidth prediction algorithm that can accommodate various business scenarios, achieving bandwidth forecasting capabilities from millisecond to daily granularity. The research scope includes constructing a benchmark test aligned with the real-world online bandwidth distribution and developing a bandwidth prediction algorithm for long-term and short-term forecasting.
# Strengths:
### ①Innovativeness:
The proposal to apply Large Language Models to bandwidth prediction is a novel attempt that has the potential to significantly improve the accuracy and generalization of predictions.
### ②Comprehensiveness:
The project considers the bandwidth prediction needs under different business scenarios, from millisecond to daily levels, showing the wide applicability of the algorithm.
### ③Data-driven:
The construction of the benchmark test through tail importance sampling enhances the model's representativeness of low-bandwidth areas, which is crucial for improving prediction accuracy.
# Weaknesses:
### ①Model Complexity:
Large Language Models may require substantial computational resources, which could limit the algorithm's practicality in resource-constrained environments.
### ②Real-time Performance:
The discussion on the model's real-time performance in the proposal is not detailed enough, especially in low-latency live streaming scenarios.
rating: 9
confidence: 4 |
5mE1shG2va | 【Proposal】A Large Language Model-based Bandwidth Prediction Algorithm | [
"Zheng Jiang",
"Iat Long Iong",
"Xin Chen"
] | In the streaming media industry, bandwidth prediction is vital for ensuring user experience and optimizing resources. In low-latency live streaming, it estimates network conditions in real-time, adjusts transmission strategies, and reduces stuttering and latency. For long videos, it helps adaptive algorithms intelligently select bitrates, balancing picture quality, smoothness, and buffering. In short videos, it determines video bitrate combinations for seamless HD playback, improving user retention. Bandwidth prediction also affects CDN distribution costs and efficiency by optimizing transcoding bitrates and scheduling. Accurate bandwidth prediction enhances decision-making, user experience, and technical architecture, crucial for competitiveness. Traditional algorithms struggle with long-term bandwidth variations due to user demands and network complexities. Recently, Transformer and Large Language Models (LLMs) from time-series prediction offer new solutions. Transformers excel in feature extraction and long-term dependency modeling, while LLMs adapt quickly using pre-training on large datasets. Applying these models to bandwidth prediction can greatly enhance accuracy, generalization, and real-time performance, addressing bandwidth prediction challenges more effectively. | [
"bandwidth prediction",
"time series",
"large language models"
] | https://openreview.net/pdf?id=5mE1shG2va | 3d6dnnYNC0 | official_review | 1,731,411,294,502 | 5mE1shG2va | [
"everyone"
] | [
"~Kairong_Luo1"
] | title: Novel idea
review: Strength:
1. The perspective is novel and have practical requirement;
2. The problem definition is clean, including how to measure the performance, the problem format;
3. There are clean steps to build benchmark and implement the algorithm;
Weakness:
1. The problem feature is unclear, like what is the difference between the bandwidth prediction from the other time series prediction, like stock market curve?
2. More details needed, like dataset collection. It seems non-trivial.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | zhNXBAeiFZ | official_review | 1,731,334,804,969 | 5GwEMXzBOP | [
"everyone"
] | [
"~Ziyu_Zhao6"
] | title: Review of "Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming" Proposal
review: Overview:
This proposal introduces Mini-Omni, an open-source large language model with end-to-end real-time speech interaction capabilities. Mini-Omni is designed to handle both speech input and output through a parallel generation process, enabling audio responses alongside textual outputs. Additionally, the proposal includes the VoiceAssistant-400K dataset, specifically created for training voice assistants, which addresses limitations in current open-source datasets for audio models.
Strengths:
1.Innovative Contribution to Multimodal LLMs: Mini-Omni stands out as the first open-source, fully end-to-end model capable of real-time audio interaction. This innovation fills a significant gap in multimodal LLMs and offers a new direction for open-source research in audio-based AI interactions.
2.Low Resource Requirement for Adaptation: The “Any Model Can Talk” approach allows other models to integrate speech capabilities with minimal additional data and training, making it accessible to a broader range of researchers and developers.
3.Dedicated Speech Dataset: The VoiceAssistant-400K dataset addresses the shortcomings in existing QA datasets for speech-based tasks. This specialized dataset is valuable for training high-quality voice assistants and can contribute broadly to research on audio-enabled LLMs.
Weaknesses:
Reliance on Synthesized Data from GPT-4o: The reliance on VoiceAssistant-400K for fine-tuning could lead to limitations in handling nuanced or naturalistic speech patterns, potentially impacting the model’s adaptability in real-world scenarios.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | pPt500pppw | official_review | 1,731,116,363,927 | 5GwEMXzBOP | [
"everyone"
] | [
"~Aleksandr_Algazinov1"
] | title: Innovative and relevant
review: The proposal is well-written and clearly defines the problem. Authors propose a new (Mini-Omni) model for real-time speech interaction. The motivation is clearly explained and leaves no doubt about the relevance of the study. Besides the new model, a new training approach (Any Model Can Talk) and a new dataset (VoiceAssistant-400K) are introduced in the proposal. This is impressive and indicates the high potential of the project.
rating: 10
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | l8xufMpZwM | official_review | 1,731,120,252,211 | 5GwEMXzBOP | [
"everyone"
] | [
"~Yufei_Zhuang1"
] | title: Exciting and highly relevant
review: This paper presents an exciting and highly relevant research in the field of language models. The recognition of the limitations in current academic models, especially the latency issue caused by relying on additional TTS systems for speech synthesis, is astute.
The introduction of Mini - Omni as an audio - based end - to - end conversational model capable of real - time speech interaction is a significant step forward. The proposed text - instructed speech generation method, along with the batch - parallel inference strategies, shows innovation. These not only enable real - time interaction but also manage to maintain the original language capabilities of the model with minimal degradation, which is a remarkable achievement.
The concept of "Any Model Can Talk" training method has great potential as it allows other works to build real - time interaction capabilities more easily. Additionally, the introduction of the VoiceAssistant - 400K dataset for fine - tuning models optimized for speech output further enriches the research. Overall, Mini - Omni being the first fully end - to - end, open - source model for real - time speech interaction opens up valuable opportunities for future research in human - computer conversation and related fields.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | cRcxJXy49g | official_review | 1,731,402,788,429 | 5GwEMXzBOP | [
"everyone"
] | [
"~ChenJian1"
] | title: Brief review
review: The paper introduces the Mini-Omni model, which is a significant innovation in the field of real-time voice interaction. The end-to-end design of the model reduces reliance on additional TTS systems, thereby reducing latency and improving user experience. The "Any Model Can Talk" approach provides an effective way for other models to quickly develop voice capabilities. Furthermore, the introduction of the VoiceAssistant-400K dataset provides a valuable resource for the fine-tuning of voice models. However, the complexity and resource requirements of the model are weaknesses that need to be further optimized in future work.
rating: 9
confidence: 3 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | ZdiFWIPVrq | official_review | 1,731,077,455,749 | 5GwEMXzBOP | [
"everyone"
] | [
"~Joydeep_Chandra2"
] | title: The title "Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming" is clear and accurately reflects the core idea of the project, highlighting its focus on real-time, multimodal capabilities.
review: The proposal is strong, with a clear problem definition, with an innovative methodology, and high potential impact. It demonstrates a thorough understanding of the field and offers a novel solution to a relevant problem. However, it could provide more specific details on implementation plan and specifying evaluation metrics. The grammatical structure also could be reviewed.
rating: 8
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | Rb1gOBsvHb | official_review | 1,731,401,381,773 | 5GwEMXzBOP | [
"everyone"
] | [
"~jin_wang30"
] | title: a surprising and promising proposal
review: This proposal introduces Mini-Omni, an open source end-to-end multimodal language model that supports real-time voice interaction. The model aims to achieve real-time human-computer voice conversation through two-way interaction of audio and text, solve the delay problem of current mainstream language models in voice processing, and proposes an innovative "Any Model Can Talk" training method.
Advantages：
Mini-Omni is the first open source multimodal language model that supports real-time voice input and output. It realizes voice generation in parallel with text output through the "Any Model Can Talk" method, providing a groundbreaking reference for future multimodal human-computer interaction research. In addition, the model solves the delay problem of traditional text generation and then conversion to voice through a parallel reasoning strategy, making the voice interaction experience smoother. It also introduces the VoiceAssistant-400K dataset dedicated to voice assistants, and optimizes the model using techniques such as delayed parallel generation and batch reasoning to ensure that the efficiency of voice generation is improved while maintaining text capabilities.
I believe that the architecture of Mini-Omni has the potential to adapt to other research, allowing for rapid integration of voice capabilities, and can be widely used in fields such as smart assistants and automated customer service.
Disadvantages：
The data source may have certain limitations. The VoiceAssistant-400K dataset is synthesized by GPT-4o and lacks real corpus verification, which may lead to insufficient generalization of the model to real voice environments. Future research may consider using richer actual voice datasets to further enhance the performance of the model in real voice tasks.
rating: 10
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | KVdaitE9rR | official_review | 1,731,236,851,678 | 5GwEMXzBOP | [
"everyone"
] | [
"~Diego_Cerretti1"
] | title: Interesting and relevant
review: The authors propose an open-source, end-to-end system for real-time audio-based interaction. This proposal addresses the necessity for models that can smoothly handle real-time speech. Overall, the proposal is an interesting contribution to multimodal model research, exhibiting great practical relevance.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | JECVTyykgt | official_review | 1,731,137,271,109 | 5GwEMXzBOP | [
"everyone"
] | [
"~Yuanda_Zhang1"
] | title: interesting idea
review: This work proposes Mini-Omni, which is the first fully end-to-end, open-source model for real-time speech interaction. Meanwhile, it offers potential for other models to incorporate interaction capabilities. Given the information from the proposal, we have that Mini-Omni will be a efficient multmodal large model with high speed of inference, that's quite interesting and promising in real market. However, more details about approaches and implementation need to be provided.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | H35qZ7uBe3 | official_review | 1,731,055,374,582 | 5GwEMXzBOP | [
"everyone"
] | [
"~Zhen_Leng_Thai1"
] | title: Promising Proposal for Real-Time Audio-Based Conversational Model
review: This paper proposes an audio-based end-to-end conversational model for real-time interaction, with a solid training method and dataset. However, the introduction contains a typo ("poinerr"), the structure requires improvement, and the task definition could be more clearly articulated.
rating: 9
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | Fc9qH64Srj | official_review | 1,731,341,408,499 | 5GwEMXzBOP | [
"everyone"
] | [
"~Michael_Hua_Wang1"
] | title: Review
review: The proposal describes Mini-Omni, an open-source model permitting humans to have real-time voice conversations with LLMs with minimal latency.
On the face of it, this project is quite ambitious, and if successful, it certainly stands to contribute to research in the area. However, the scope of the work to actually be done is unclear, given that the author appears to have already submitted a preprint of this exact research topic to Arxiv a few months ago. Is the proposal here to do additional refinement following the methodology previously used for the pre-print, or is there additional work to be done?
This proposal could stand to clearly delineate the scope of the work with the above in mind.
rating: 8
confidence: 3 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | 9L2dbXt3Pd | official_review | 1,731,304,758,297 | 5GwEMXzBOP | [
"everyone"
] | [
"~Rim_El_Filali1"
] | title: Promising Open-Source Real-Time Speech Model with Evaluation Gaps
review: This paper presents Mini-Omni, an innovative open-source, multimodal language model with real-time speech interaction capabilities, bridging a gap in current language models’ abilities to engage in real-time audio conversations. Additionally, it introduces a specialized dataset, VoiceAssistant-400K, specifically tailored for training speech assistants.
Pros:
- Batch-parallel inference and delayed parallel generation effectively reduce latency, enhancing real-time interaction.
- The "Any Model Can Talk" training method adds audio capabilities to models with minimal overhead.
Cons:
- The proposal lacks a clear flow in places, particularly in separating high-level objectives from technical details, which may hinder readability.
- More empirical data on performance benchmarks would enhance the paper’s impact.
rating: 8
confidence: 4 |
5GwEMXzBOP | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | [
"Xiezhifei"
] | Recent advances in language models have achieved significant progress. GPT-4o,
as a new milestone, has enabled real-time conversations with humans, demonstrat-
ing near-human natural fluency. Such human-computer interaction necessitates
models with the capability to perform reasoning directly with the audio modality
and generate output in streaming. However, this remains beyond the reach of
current academic models, as they typically depend on extra TTS systems for speech
synthesis, resulting in undesirable latency. This paper introduces the Mini-Omni,
an audio-based end-to-end conversational model, capable of real-time speech inter-
action. To achieve this capability, we propose a text-instructed speech generation
method, along with batch-parallel strategies during inference to further boost the
performance. Our method also helps to retain the original model’s language ca-
pabilities with minimal degradation, enabling other works to establish real-time
interaction capabilities. We call this training method "Any Model Can Talk". We
also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential for
future research. | [
"Large Multimodal Models",
"GPT4o"
] | https://openreview.net/pdf?id=5GwEMXzBOP | 8F3z2wgilR | official_review | 1,731,038,313,080 | 5GwEMXzBOP | [
"everyone"
] | [
"~Lily_Sheng1"
] | title: Submission 26 Review
review: The Mini-Omni project presents a novel approach to enabling end-to-end real-time conversational capabilities in a large language model, allowing multimodal interaction by incorporating both audio input and streaming audio output.
Pros:
1. The model's design focuses on a lightweight 0.5B architecture and a limited amount of synthesized audio data, allowing robust multimodal capabilities without heavy computational demands.
2. The VoiceAssistant-400K dataset fills in a gap in current open-source resources.
Cons:
1. The VoiceAssistant-400K dataset uses GPT-4o for generation which may introduce bias or repetitive phrasing patterns.
2. There could be more details on the methodology and approaches.
rating: 9
confidence: 4 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | t6q0u5ynUn | official_review | 1,731,086,516,037 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Yanchen_Wu1"
] | title: Great research problem and solid theoretical basis
review: In this paper, the author integrates safe learning theory into offline reinforcement learning to make up for the weakness of offline reinforcement learning. In the appendix, the author also shows solid theoretical preparation and algorithm design. I am very confident that this will be a very good research.
rating: 8
confidence: 5 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | qrFLPUreXA | official_review | 1,731,398,199,176 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Zhixuan_Pan1"
] | title: Review
review: This proposal introduces Dataset-Constrained Reinforcement Learning (DCRL) for safe policy learning in offline RL. By implementing a Dataset Feasibility Function (DFF), the approach limits policy exploration within dataset boundaries while managing out-of-distribution (OOD) actions to improve both safety and performance.
Pros:
1.Innovative integration of safe RL into offline RL, enhancing policy robustness within constrained environments.
Dual mechanism (DFG and DFI) effectively balances safe exploration with performance in offline RL tasks.
Cons:
1. This is more like a well-developed project rather than a proposal for new work.
2. The optimization process after introducing constraints seems consistent with the standard Lagrange multiplier method. However, this is neither mentioned in the related work section nor addressed in the theoretical analysis.
rating: 9
confidence: 3 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | UtrepiDaqa | official_review | 1,731,424,662,953 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Suraj_Joshi2"
] | title: Excellent Proposal
review: Everything perfect, my only concern is that there might some avalanche effect due when one agent makes an error that error would be propagated to other agents and hence enlarged, besides that well articulated....all the best!! Excited to see project in action!!
rating: 10
confidence: 4 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | MDx95iyYz2 | official_review | 1,731,114,330,395 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Xiying_Huang2"
] | title: Innovative Dataset-Constrained Approach Enhances Safety in Offline Reinforcement Learning
review: This paper introduces a novel Dataset-Constrained Reinforcement Learning (DCRL) framework aimed at improving safety and effectiveness in offline reinforcement learning by constraining out-of-distribution (OOD) actions. The approach employs Dataset Feasibility Guidance (DFG) and Dataset Feasibility Indication (DFI) mechanisms, which guide policy learning within safe dataset boundaries. Extensive benchmark testing demonstrates that DCRL outperforms traditional methods in both safety and policy efficacy.
Quality:
The paper is technically sound, presenting a well-structured approach to enhancing safety in offline reinforcement learning.
Clarity:
The authors provide clear explanations of the DCRL components and empirical results that validate the framework’s effectiveness.
Originality:
The use of feasibility functions for safe OOD exploration marks a novel contribution to offline RL research.
Significance:
DCRL shows strong potential for applications in safety-critical domains, such as robotics and autonomous systems.
Pros:
• Novel approach with a strong safety focus in offline RL.
• Comprehensive empirical support showcasing improved performance and safety.
• Potential impact on real-world applications in risk-sensitive environments.
Cons:
• Feasibility function dependency on pre-existing datasets could limit adaptability.
• Further discussion on computational requirements and parameter adjustments would enhance reproducibility.
rating: 8
confidence: 4 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | JsolXxB2NE | official_review | 1,731,226,881,833 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Xun_Wang10"
] | title: Review for "DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration"
review: This paper introduces a Dataset-Constrained Reinforcement Learning (DCRL) framework for offline RL, which combines safe RL principles with regularization and OOD detection mechanisms to ensure safe and effective policy learning within dataset constraints.
Strength: The proposal thoroughly explains the problem background, related work, and algorithm principles. The experiments are also carefully designed. Moreover, the idea of applying safe RL theory to the offline RL domain is highly innovative.
Weakness: For a proposal, the length and certain content may be somewhat excessive.
rating: 9
confidence: 4 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | IIxbCr3LzU | official_review | 1,731,418,021,618 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: Review for "DCRL"
review: **Summary:**
DCRL introduces safe reinforcement learning theory into offline RL, proposing the Dataset Feasibility Function (DFF) to manage OOD risks. Through Dataset Feasibility Guidance and Dataset Feasibility Indication mechanisms, it achieves safe exploration under dataset constraints and demonstrates superior performance on D4RL benchmarks.
**Highlights:**
- Novel integration of safe RL and offline RL theories
- Introduction of DFF to evaluate state-action pair feasibility
- Dual-mechanism design (DFG/DFI) for safe exploration
**Advice:**
Given the comprehensive experimental results already present, suggest focusing on enhancing the experimental section, add ablation studies to analyze individual contributions of DFG and DFI.
rating: 9
confidence: 3 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | FEEgOJqskA | official_review | 1,731,056,627,174 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Zhen_Leng_Thai1"
] | title: Detailed and Innovative Proposal with Strong Concepts
review: This paper presents a novel Dataset-Constrained Reinforcement Learning (DCRL) framework, featuring Dataset Feasibility Guidance (DFG) for policy regularization and Dataset Feasibility Indication (DFI) for OOD detection—both valuable contributions to offline reinforcement learning. Additional rationale for selecting legged robots or toy car tracking as testing scenarios would be beneficial. However, the paper exceeds the two-page proposal requirement by an additional seven pages, violating submission rules.
rating: 8
confidence: 3 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | CRMk6mLNQX | official_review | 1,730,894,766,180 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Shaoting_Zhu1"
] | title: Review of submission 29
review: The paper introduces a novel approach in the field of offline reinforcement learning (RL) that addresses the critical issue of out-of-distribution (OOD) actions. The authors propose the Dataset-Constrained Reinforcement Learning (DCRL) framework, which integrates safe RL theory into offline RL by leveraging the Dataset Feasibility Function (DFF). This function assesses the feasibility of state-action pairs within dataset constraints, guiding policy learning to respect dataset boundaries while managing OOD risks. The DCRL framework employs two mechanisms: Dataset Feasibility Guidance (DFG) and Dataset Feasibility Indication (DFI), which serve as a regularization term and an OOD detection tool, respectively. The approach aims to blend safety constraints with regularization and counterfactual reasoning to enhance performance and robustness in offline RL.
**Strength**
1. Innovative Integration of Offline RL and Safe RL Theory: The paper presents a pioneering integration of safe RL principles directly into the offline RL OOD problem, providing a structured approach to mitigate OOD risks and promote safe policy learning.
2. Clear task and metric: At the end of the proposal, the author plans to test the algorithm in four different datasets/agents.
3. Detailed survey and method: The paper has a very detailed method with preliminaries, theorems, and pseudo codes. And the related works and baselines to compare are clearly proposed.
**Weakness**
It's too long for a proposal. The page limit is severely exceeded. This leads to the core method being hard to catch, and there is no figure to illustrate the method. Based on this detailed proposal, I think the author may have already done many works on this project, but the majority of the article is not feasible for proposal.
**Questions**
The proposed method relies highly on datasets. However, in many cases, we have no sufficient dataset. For example, the legged robot Go1, the popular method is to train from scratch, and we will have no data before it can walk. I think it is like the problem "The chicken or the egg first." Also, the performance of the final policy highly depends on the performance of the dataset because the algorithm always finds the nearest action in the dataset. This may be missing some of the original purpose of reinforcement learning, which is to learn by exploring. This pattern can make it harder to develop new strategies such as extreme locomotion (jumping to a high box) of the legged robot.
rating: 8
confidence: 4 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | 2BXtiRTTbQ | official_review | 1,730,895,800,239 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Yinuo_Li1"
] | title: Clear problem definition and solid research
review: This proposal presents a promising approach to safe offline RL by integrating a dataset feasibility function to manage OOD risks. The methodology is sound, and empirical results are strong. Comprehensive review of related works are given and very detailed method and experiments are showed.
Further discussion on computational complexity and generalization to other domains would be beneficial.
rating: 9
confidence: 3 |
4DUHGxUu8Q | [Proposal-ML] DCRL: Dataset-Constrained Reinforcement Learning for Safe In-Distribution Exploration | [
"Ziang Zheng"
] | In offline reinforcement learning (RL), addressing out-of-distribution (OOD) actions is essential for safe policy learning, as such actions often lead to overestimated values and risky behaviors. Existing methods primarily tackle this issue through regularization or counterfactual reasoning but often lack a principled approach to guarantee safe exploration within dataset constraints. This paper presents a novel approach that incorporates safe RL theory into offline RL by introducing the Dataset Feasibility Function (DFF), enabling policy learning that respects dataset boundaries while managing OOD risks. Our proposed Dataset-Constrained Reinforcement Learning (DCRL) framework employs two mechanisms: Dataset Feasibility Guidance (DFG), which serves as a regularization term to keep the policy aligned with the dataset distribution, and Dataset Feasibility Indication (DFI), which acts as an OOD detection tool. DFI enables safe out-of-distribution exploration by leveraging model rollouts constrained within feasible zones identified by a larger tolerance threshold. This approach uniquely blends safety constraints with both regularization and counterfactual reasoning to advance performance and robustness in offline RL. Empirical evaluations on benchmark datasets validate that DCRL outperforms existing methods, achieving superior safety and efficacy in constrained offline tasks. | [
"Reinforcement Learning",
"Offline Reinforcement Learning",
"Safe Reinforcement Learning",
"OOD"
] | https://openreview.net/pdf?id=4DUHGxUu8Q | 0WTw4AxdxA | official_review | 1,731,308,568,713 | 4DUHGxUu8Q | [
"everyone"
] | [
"~Gausse_Mael_DONGMO_KENFACK1"
] | title: Innovative and Good Research Work
review: This paper introduces Dataset-Constrained Reinforcement Learning (DCRL), a framework that integrates safe RL principles into offline RL. The main innovation is the Dataset Feasibility Function (DFF), which helps guide policy learning within dataset boundaries, addressing out-of-distribution (OOD) risks inherent in offline RL. The DFF is implemented via two complementary strategies: Dataset Feasibility Guidance (DFG), which regularizes policies to stay within dataset constraints, and Dataset Feasibility Indication (DFI), an OOD detection mechanism allowing controlled exploration of feasible but riskier regions.
strengths : The dual mechanism of DFG and DFI is well-formulated, presenting a novel way to manage OOD actions. The approach is validated on multiple benchmarks.
weakness: The proposal is too long and does not really respect the guidelines.
rating: 8
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | z37STlNd5E | official_review | 1,731,119,771,203 | 3NTMkz3lHG | [
"everyone"
] | [
"~Yufei_Zhuang1"
] | title: Great potential and well formed
review: This project shows great potential in the field of robot learning. By focusing on dataset augmentation with diffusion models, it tackles a significant issue - the scarcity of large - scale and diverse datasets in robotics.
The approach of background scene manipulation is clever. It's an innovative way to generate new samples within existing datasets, which can be crucial for improving the generalization of learned policies. This has far - reaching implications for various robot learning tasks as it can expose the models to more diverse scenarios.
Using a combination of real - world, synthetic, and human - centric datasets is also a strong point. This diverse data source can enrich the training process and potentially lead to more robust models. Overall, this project seems to be on a promising path to enhance robot learning capabilities.
rating: 9
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | uvO4lj4355 | official_review | 1,731,418,385,329 | 3NTMkz3lHG | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: mocking dataset generation task
review: **Strengths:**
* Clear technical pipeline with practical implementation steps
* Good use of modern tools (ControlNet, SAM) and diverse data sources
* Well-structured methodology for maintaining object/robot consistency
**Concerns:**
1. No discussion of how to ensure physical plausibility in generated scenes
2. Missing analysis of potential impact on policy learning - could complex backgrounds hurt performance?
3. Unclear evaluation framework for measuring generalization benefits
**Recommendations:**
1. Include methods to verify physical consistency in generated scenes
2. Develop clear validation approach for testing policy network
rating: 9
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | ucvj7Efr2M | official_review | 1,731,396,590,144 | 3NTMkz3lHG | [
"everyone"
] | [
"~Jiuyang_Zhou1"
] | title: creative and sufficient work
review: This paper proposes using diffusion models to augment existing datasets by manipulating background scenes, aiming to address the issue of the lack of large-scale and diverse datasets in the field of robotics. Its main contribution lies in presenting a new scene augmentation method, which is specifically implemented through a segmentation model and a generative model. First, a dataset of <RGB image, segmentation mask> pairs is created. Then, the models are fine-tuned and the CycleGAN framework is utilized to ensure consistency. Finally, the dataset is augmented. The paper employs a variety of resources, including datasets such as Open - X - Embodiment, segmentation labeling methods like manual annotation, and diffusion models such as ControlNet. However, this method may face some challenges, such as the effectiveness and training difficulty of the generative model, and the compatibility of the new scenes with the real world. Overall, it provides an innovative data augmentation idea for the field of robot manipulation learning and is expected to enhance the generalization ability and robustness of the policy network in downstream tasks.
rating: 9
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | gUkCjxRE4c | official_review | 1,731,296,350,524 | 3NTMkz3lHG | [
"everyone"
] | [
"~Junjie_Chen8"
] | title: Good Proposal
review: The proposal provides a clear and well-structured approach to addressing a significant challenge in robotics: augmenting datasets to improve manipulation learning. The use of diffusion models for scene augmentation and the combination of real-world, synthetic, and egocentric human datasets make the methodology both innovative and practical. The pipeline is systematically designed, incorporating segmentation and generative models to enhance dataset diversity and robustness. What's more, I think a clearer description of evaluation metrics would strengthen the proposal.
rating: 8
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | ZIp70xrA5b | official_review | 1,731,413,896,020 | 3NTMkz3lHG | [
"everyone"
] | [
"~Kairong_Luo1"
] | title: Interesting dataset generation
review: Strength:
1. Complete preparation: dataset basics, and so on;
2. A interesting combination of diffusion model and segmentation models;
Weakness:
1. Questions occur to me that why does diffusion model bypass the sim-real gap shown in other synthetic data?
2. What is the advantages about fixing the segmentation masks than other options? Because it seems fix the position of objects in the scenes?
rating: 8
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | UzaVnAQ0TQ | official_review | 1,731,324,537,165 | 3NTMkz3lHG | [
"everyone"
] | [
"~Zhang_Mingkang1"
] | title: Novel idea
review: Strengths:
Background
Excellent identification of the data scarcity problem in robotics.
Clear connection to foundation models and their limitations.
Strong motivation for bridging sim-to-real gap.
Well-articulated research challenge.
Definition
Precise definition of the transformation from D to Da.
Well-defined policy learning objective π(Da).
Related Work
Comprehensive review of visual data augmentation techniques.
Excellent critique of existing methods' limitations.
Strong analysis of current approaches (GreenAug, diffusion models).
Clear identification of research gaps.
Proposed Method
Innovative three-step pipeline.
Well-thought-out dataset combination strategy.
Clear technical approach using state-of-the-art tools.
Detailed resource survey and implementation plan.
Key Comments:
Outstanding integration of multiple data sources (real-robot, synthetic, human).
Novel use of diffusion models for background generation.
Practical approach to segmentation using modern tools.
Well-structured experimental design.
Excellent consideration of data quality and diversity.
Areas for Improvement:
Might benefit from quantitative metrics for evaluating generated scenes.
Consider discussing computational requirements for training.
Could elaborate on potential failure modes.
This is an exceptionally well-crafted proposal that addresses a fundamental challenge in robot learning. The approach is innovative, practical, and well-grounded in current literature.
rating: 9
confidence: 3 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | TE9mWcvHSU | official_review | 1,731,057,380,572 | 3NTMkz3lHG | [
"everyone"
] | [
"~Zhen_Leng_Thai1"
] | title: Great and Detailed Proposal with Outstanding Ideas
review: This paper introduces an innovative method using diffusion models to augment datasets by manipulating background scenes. This method includes a segmentation model for mask segmentation and generative model for image generation. The background and mathematical definitions are well-defined. Relation works about segmentation, diffusion models and visual data augmentation is comprehensive. The proposed method is structured in three steps: dataset creation, model fine-tuning, and model integration, with additional details on dataset resources, segmentation labeling, and diffusion model specifics.
rating: 10
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | ROMUhX7ZS7 | official_review | 1,731,414,813,980 | 3NTMkz3lHG | [
"everyone"
] | [
"~Justinas_Jučas3"
] | title: Unique Idea and Solid Proposal
review: In general, the proposal is ambitious, and for sure original. It is also very well structured, with added visualizations, which make it easier to view.
## Advantages
1. Well structured and easy to read. All requirements satisfied.
2. Original idea
3. Without a doubt, achievable within the time constraints
4. Detailed pipeline explanaition: already providing a clear idea of how the algorithm would work
## Disadvantages
It is not really mentioned how the influence of your augmented data will be evaluated. How will you know that your technique gives positive results for actual robot training? Perhaps this is not within the scope of the proposal, but anyone can slightly augment any image, but what matters is how it contributes to the end goal (in our case it is better-performing robots? or perhaps something else, maybe you only want to diversify the dataset).
Without clear evaluation metrics, how will you make sure that the way you augment the dataset does not, for instance, disbalance it, reducing the actual important features it had?
rating: 8
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | Mg1M19KUa7 | official_review | 1,731,396,687,212 | 3NTMkz3lHG | [
"everyone"
] | [
"~Chaoqun_Yang2"
] | title: A meaningful proposal
review: **Summary:**
The paper tackles the critical issue of dataset diversity in robotics, which is essential for developing generalized robotic manipulation models. The problem is rooted in practical applications, particularly in the deployment of robots in varied and dynamic real-world environments. Solving this problem could significantly impact the field by enabling robots to perform tasks across different settings more effectively. The proposed method involves using diffusion models to augment datasets by manipulating background scenes, aiming to train a more generalized policy network for robotic manipulation tasks.
**Highlights:**
1. **Diffusion Models for Scene Augmentation:** The use of diffusion models for scene augmentation is innovative and represents a significant departure from traditional data augmentation techniques. This approach could potentially bridge the gap between simulation and real-world applications, a notorious challenge in robotics.
2. **Comprehensive Dataset Strategy:** The plan to utilize a combination of real-world, synthetic, and egocentric human video datasets is commendable. This strategy could result in a more robust and versatile model capable of handling various manipulation tasks.
**Advice:**
1. **Detailed Comparison with Related Work:** While the paper mentions related works, a more detailed analysis of existing approaches, their limitations, and how the proposed method overcomes these limitations is necessary. This should include a discussion on the sim2real transfer problem and how the proposed method addresses it.
2. **Experimental Design and Baseline Comparisons:** The paper would be better if presenting more detailed experimental design, including the the baseline in this area, the metrics for evaluating the method's performance, and some sub-experiments to explore the effectiveness of different modules . It is crucial to describe how the proposed method will be implemented and validated against these baselines.
rating: 8
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | KJSeTG2OzU | official_review | 1,731,113,959,634 | 3NTMkz3lHG | [
"everyone"
] | [
"~Xiying_Huang2"
] | title: Innovative Scene Augmentation for Enhanced Robot Manipulation Learning
review: This paper proposes a method for enhancing robot manipulation learning through scene augmentation. The approach focuses on using diffusion models to expand existing datasets by altering background scenes, thereby improving policy networks for diverse robotic tasks.
Quality: The methodology is clear and methodical, incorporating segmentation and generative models. Each stage of the process is detailed and feasible.
Clarity: The paper is generally well-structured. However, technical aspects, such as specific model training details, could be expanded for further clarity.
Originality: The proposal is innovative in its use of diffusion models for generating diverse robotic training environments, contributing novel insights into dataset augmentation for sim-to-real transfer.
Significance: Given the challenges of sim-to-real transfer in robotics, this research is relevant and could have substantial impacts on the field.
Pros:
• Addresses a key challenge in robotic manipulation learning.
• Proposes a feasible method with clear steps.
• Utilizes a variety of data sources to ensure robust model training.
Cons:
• Limited discussion on potential limitations of the diffusion models.
• Further clarification on quantitative evaluation metrics would be beneficial.
rating: 10
confidence: 4 |
3NTMkz3lHG | [Proposal-ML] End-to-End Scene Augmentation for Robust Robot Manipulation Learning | [
"Chengbo Yuan",
"Shaoting Zhu",
"Suraj Joshi"
] | This project aims to enhance robot learning through dataset augmentation using diffusion models, and address the limitation of scarce large-scale, diverse datasets in robotics. Our method involves background scene manipulation to generate new samples in existing datasets, aiming to improve the generalization of learned policies for various robot learning tasks. The pipeline includes training segmentation and generative models to change the background of images while keeping the robot arm and object parts consistent. A combination of real-world, synthetic, and human-centric datasets is used to train the models. | [
"Robot Manipulation",
"diffusion model",
"segmentation model",
"dataset augmentation"
] | https://openreview.net/pdf?id=3NTMkz3lHG | Ff1xr0jGef | official_review | 1,731,310,965,031 | 3NTMkz3lHG | [
"everyone"
] | [
"~Chengming_Shi1"
] | title: Review
review: ### Summary
The proposal “End-to-End Scene Augmentation for Robust Robot Manipulation Learning” aims to address the lack of diverse datasets in robotics by using diffusion models to augment existing datasets with varied background scenes. The goal is to enhance the generalizability of robot manipulation policies across a wide range of tasks by training on an augmented dataset that reflects a broader set of environmental conditions.
### Pros
1. **Innovation in Data Augmentation**: The use of diffusion models for scene augmentation is a novel approach that could significantly improve the diversity and robustness of robot learning datasets.
2. **Potential for Generalization**: By augmenting the dataset with a variety of backgrounds, the trained policies are more likely to generalize to new environments, reducing the sim-to-real gap.
3. **Integration of Real and Synthetic Data**: The proposal’s strategy of combining real-world robot images, synthetic robot images, and egocentric human images could lead to a rich and varied training dataset.
4. **Custom Segmentation Model**: Training a custom segmentation model for robot and object masks indicates a commitment to high-quality data preprocessing, which is crucial for the success of the generative model.
5. **Advanced Generative Models**: The use of ControlNet and Uni-ControlNet for conditional image generation suggests that the proposal is leveraging state-of-the-art technology to achieve its goals.
### Cons
1. **Complexity of Implementation**: The proposed method involves multiple steps, including dataset creation, model training, and augmentation, which could be complex and time-consuming to implement.
2. **Dependency on High-Quality Masks**: The success of the generative model heavily relies on the accuracy of the segmentation masks, which may be challenging to obtain, especially for real-world data.
rating: 10
confidence: 3 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | up7Yuj4FXC | official_review | 1,731,043,452,936 | 1xjVy9oyPX | [
"everyone"
] | [
"~Ethan_Wei_Yuxin1"
] | title: Well done!
review: Your proposal presents a compelling approach to enhancing material property prediction by addressing key limitations in current graph neural network (GNN) models. You effectively highlight challenges such as the difficulty of capturing global features and assigning individual contributions to atoms within materials. Your proposed transformer model, with its attention mechanisms, offers a thoughtful solution to these challenges, aiming to improve accuracy in predicting properties that depend on global structural features, like piezoelectric and dielectric constants. The step-by-step methodology— including attention analysis, model training, and the demonstration of global information capture—shows a well-planned strategy that leverages transformers' strengths for this domain. This is a promising direction, and I look forward to seeing how your work advances the field of material property prediction.
rating: 8
confidence: 3 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | uPLfBG716W | official_review | 1,731,293,680,115 | 1xjVy9oyPX | [
"everyone"
] | [
"~Tianxing_Yang1"
] | title: Evaluating the Use of Transformer Models for Predicting Material Properties
review: This proposal suggests using transformer models to predict the properties of materials.
Pros:
- Utilizing machine learning methods for material property prediction represents a novel research paradigm with significant potential.
- The proposed methodology section outlines a fairly detailed, step-by-step research plan.
Cons:
- The application of attention mechanisms to graph-related tasks has been widely explored in previous studies [1][2][3]. If the author intends to introduce innovation in the model architecture, it would be beneficial to discuss how the proposed approach differs from existing methods in the final paper.
- Addressing issues such as the assessment of global symmetry in materials could be approached by treating it as a downstream application of graph matching tasks.
References:
[1] Petar Veličković et al., Graph Attention Networks
[2] Seongjun Yun et al., Graph Transformer Networks
[3] Chengxuan Ying et al., Do Transformers Really Perform Bad for Graph Representation?
rating: 9
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | phcqJchoNh | official_review | 1,731,058,659,607 | 1xjVy9oyPX | [
"everyone"
] | [
"~Zhen_Leng_Thai1"
] | title: Well-Structured Proposal on Transformer-Based Material Properties Prediction
review: This paper proposes a transformer model with an attention mechanism to predict material properties, addressing accuracy issues in GNN models for this task. The problem definition is clear, and the proposed method is well-structured. However, the related works section overlooks some relevant transformer-based studies, such as Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction.
rating: 9
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | n142jNK9Rw | official_review | 1,731,402,729,256 | 1xjVy9oyPX | [
"everyone"
] | [
"~Chaoqun_Yang2"
] | title: Good questions and appropriate methods
review: **Summary:**
The proposal presents an innovative approach to predicting material properties using transformer models and exploring the explainability of the model according to attention weights.
**Highlights:**
1. **Clear Problem Introduction:** The proposal clearly articulates the limitations of current GNN models in materials science, specifically their challenges in assigning atomic contributions and capturing global features of crystal structures. This sets a strong foundation for the proposed research.
2. **Appropriate Technical Selection:** The choice of transformer models is well-justified, given their success in handling long-range dependencies and global context in other domains. This selection is appropriate for addressing the identified limitations of GNNs in materials property prediction.
3. **Logical Method Design:** The methodology is logically designed, starting from model training with substantial datasets to analyzing attention weights and demonstrating the model's global view capabilities. This step-by-step approach is sound and addresses the research objectives effectively.
**Advice:**
1. **Model Reliability:** A critical aspect to address is the reliability of the transformer model's predictions. The proposal hinges on the premise that transformers can accurately predict material properties. It is essential to validate the model's prediction accuracy rigorously. If the transformer model's accuracy is mediocre, the insights derived from attention weights may be unreliable.
rating: 10
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | mOXJkw9GP7 | official_review | 1,731,344,352,337 | 1xjVy9oyPX | [
"everyone"
] | [
"~Liu_Yiyang1"
] | title: Innovative and well pitched project
review: This paper proposes the use of transformer models in predicting material properties, which is an area currently dominated by GNN models. It presents a compelling argument towards the potential benefits of using transformer models, diving into great detail regarding the shortcomings of GNN models and how these shortcomings can be mitigated with the use of transformer models. The proposed methodology also included concrete details on areas like datasets, design methodologies and potential optimization strategies. One potential area of concern is that accurately capturing 3D spatial relations and symmetry into a 1D positional encoding scheme accurately might be tricky and need to be extensively validated in the future. Otherwise, very well pitched and thoroughly research proposal!
rating: 9
confidence: 3 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | lSKGXGxsrk | official_review | 1,731,229,450,780 | 1xjVy9oyPX | [
"everyone"
] | [
"~Chan_Thong_Fong1"
] | title: Innovative idea to implement transformer models in predicting material properties
review: The proposed method offers a compelling advancement in material property prediction by leveraging transformers, which have the potential to overcome key limitations of current graph neural network (GNN)-based models. This approach could significantly enhance the interpretability and accuracy of predictions compared to GNN models, which struggle with global feature extraction and atom-specific contributions. Additionally, the proposed framework’s flexibility in handling input size and its potential for discovering new materials make it an exciting avenue for future material science research. However, the method’s success will depend on the effective integration of structural encoding and attention analysis, which will need to be rigorously validated through comprehensive experimentation on large, diverse datasets.
rating: 8
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | Qa3mulZx2Q | official_review | 1,731,064,015,907 | 1xjVy9oyPX | [
"everyone"
] | [
"~Un_Lok_Chen1"
] | title: A Transformer-based Model that Better Captures Global Features in Predicting Material Properties
review: Summary:
This proposal explores the potential of Transformer models in predicting material properties. The authors suggest that compared with GNNs, Transformer models may excel in capturing global features, dealing with variable numbers of atoms in different molecules, and attributing global property contribution to individual atoms. They attempt to adopt some attention analysis techniques to better understand how the Transformer models may be useful.
Pros:
1. In the Background section, the authors present the problem context with abundant examples accompanied by clear explanations, thus making the reasoning about using Transformer to better capture global features of the molecule easy to follow.
2. The proposal includes a detailed description of potential analysis (e.g. attention weight visualization) that can be conducted, which seems reasonable enough; the step-by-step implementation plan is also well-thought-out.
Cons:
A. Major issues
1) Are there any existing works that also adopt the Transformer model approach to predict the material properties or in similar scientific tasks?
B. Minor issues
1) The caption for Figure 1 can be more specific.
2) Please include in-text citations for the figures if they are adopted from other works.
3) The “structural encoding” method mentioned in the Model training part of the Proposed Method section may require further elaboration if it is a crucial component of the model.
4) Would comparison experiments be conducted with the proposed Transformer model (e.g. the GNN architecture w/ attention mechanism as mentioned in the Related Work section)?
rating: 9
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | KbY1u1zTeh | official_review | 1,731,144,703,800 | 1xjVy9oyPX | [
"everyone"
] | [
"~André_Moreira_Leal_Leonor1"
] | title: Good job
review: The proposal effectively argues for using transformer models to improve material property prediction, addressing GNN limitations in capturing atomic contributions and global features. It’s well-researched, with strong points on transformer benefits for handling global structures and input flexibility.
Strengths:
Clear articulation of GNN challenges and transformer advantages.
Solid related work and methodology.
Areas to Improve:
Explain how global symmetry will be encoded.
Clarify performance metrics and address transformers' computational demands.
Suggestions:
Add a specific example, like predicting bandgap, to illustrate the approach.
rating: 9
confidence: 4 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | 9XLd0Lxw14 | official_review | 1,731,418,753,751 | 1xjVy9oyPX | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: meaningful material properties predicting model
review: **Highlights**
* Clearly identifies two key limitations of current GNN models, demonstrating their practical impact through specific cases such as perovskites.
* The attention mechanism naturally matches the needs of materials science, with solutions supported by theoretical foundation and feasibility.
**Advice**
* Suggest supplementing theoretical analysis of how Transformer handles 3D spatial data, and explain how to integrate crystallographic knowledge into the model architecture.
rating: 10
confidence: 3 |
1xjVy9oyPX | 【Proposal】Transformer Models for Predicting Material Properties | [
"Kaiwei Zhang",
"Juncheng Yu"
] | Graph neural networks (GNNs) have been widely used to predict material properties. However, current GNN models cannot assign the contribution of each atom. Meanwhile, GNNs are poor at capturing global information, resulting in unsatisfactory predictions of certain properties. The widely used transformer may be suitable for crystals and could be used to tackle both problems. We propose to train a transformer for predicting crystal properties, followed by analyzing learned attentions to assign contributions and demonstrating the model’s global view. Further discussions could be made if successful. In terms of related works, we introduce research about proposed models, usage of attention mechanisms, common model variants as well as network applications. | [
"Transformer",
"Attention mechanism",
"Graph Neural Network"
] | https://openreview.net/pdf?id=1xjVy9oyPX | 72LHFsIv5j | official_review | 1,731,414,650,447 | 1xjVy9oyPX | [
"everyone"
] | [
"~Kairong_Luo1"
] | title: Interdisciplinary idea
review: Strength:
1. quite an interdisciplinary idea;
2. a detailed description about the problem setting and possible approaches;
3. the related work review is very thorough.
Weakness:
1. i am curious about the paradigm in this field. It seems just mapping the material problem into a graphical problem. Can more material information be encoded into the structure or embedding? i do not know whether there is some hint.
2. the advantage in LLM seems not explained in details. A noteworthy problem is that the LLM needs great data, even in fine-tuning. Maybe the volume of dataset could be shown.
rating: 8
confidence: 2 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | rRR0pwewZD | official_review | 1,731,305,909,841 | 1tVpbWoJRX | [
"everyone"
] | [
"~Kehan_Zheng1"
] | title: Nice Proposal
review: The proposal presents an interesting approach to stock market prediction using a hybrid model called Jamba, which combines Mamba and Transformer architectures. The structure of the proposal is clear, covering background, problem definition, methodology, and evaluation metrics, aiming to solve traditional machine learning problems. However, the project could be further enriched by exploring more creative solutions or by applying the model to datasets or scenarios with greater real-world relevance. This would not only add depth to the analysis but also enhance the project’s overall impact and meaningfulness.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | kmdiVOderw | official_review | 1,731,113,572,103 | 1tVpbWoJRX | [
"everyone"
] | [
"~Xiying_Huang2"
] | title: Review of “Machine Learning Project Proposal”
review: This proposal outlines a promising and innovative approach to stock market forecasting by implementing the Jamba model, which integrates transformer and Mamba architectures. The methodology is well-structured and geared toward optimizing performance in a Kaggle competition, with clear steps for data preprocessing, model training, and evaluation. The proposal’s relevance to financial forecasting highlights its potential real-world impact, especially in handling complex, high-dimensional data. However, further clarification on the unique strengths of the Jamba model and a more detailed plan for managing financial data challenges would strengthen its contribution.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | cKscHIhWgP | official_review | 1,731,118,777,901 | 1tVpbWoJRX | [
"everyone"
] | [
"~Yufei_Zhuang1"
] | title: Shows great promise
review: The choice to rely solely on the provided Kaggle dataset from Jane Streets' production systems is reasonable given their belief in its sufficiency. This simplifies the data - related complexity and allows the team to focus more on model development.
The evaluation approach of using the competition's scoring formula to measure against other participants is practical. It streamlines the process of determining the model's effectiveness without getting bogged down in extensive comparative analyses of other models independently.
The implementation plan is structured and comprehensive. The data preprocessing steps, including cleaning, scaling, and handling time - series data with feature windows, are standard yet crucial for good model performance. The training process with cross - validation for hyperparameter optimization shows a solid understanding of model tuning. Using the competition's scoring metric R2 as the primary evaluation metric, along with potential use of MAE and MSE, provides a good balance in assessing the model's quality. Overall, the proposed method appears to be a strong contender in the Kaggle competition.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | YtR1NNsxbH | official_review | 1,731,417,502,046 | 1tVpbWoJRX | [
"everyone"
] | [
"~Bowen_Gao1"
] | title: Review
review: **Summary**
This proposal addresses the Jane Street stock market prediction challenge on Kaggle, where the authors suggest using "Jamba," a novel Mamba-Transformer hybrid machine learning model, to improve prediction performance.
**Strengths:**
1. The motivation for using Jamba is well-justified, as its ability to capture complex temporal dependencies and adaptability to high-volume, real-time data aligns with the demands of stock market prediction.
2. The proposal provides a clear outline of the pipeline, covering key elements such as model training, dataset selection, and model evaluation.
**Weaknesses:**
1. The proposal lacks a correct title that is relevant to the content of the proposal.
2. The proposed method is relatively simplistic, involving a straightforward application of an existing model to the task without significant adaptation or innovation.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | VMdTujztVl | official_review | 1,731,330,061,780 | 1tVpbWoJRX | [
"everyone"
] | [
"~Ruowen_Zhao1"
] | title: Summary and Concern
review: ### Summary
The authors describe a structured approach to implement the Jamba model on the Kaggle dataset. Their process involves three main stages: data preprocessing, model training, and evaluation.
### Concern
+ Lack of data analysis: The outlined approach lacks a thorough analysis of the input data. Without this step, the preprocessing and model training stages may miss important characteristics of the data.
+ Lack of analysis on training model selection: The approach does not provide a detailed analysis for choosing the Jamba model as the primary training model. Without evaluating alternative models or explaining why Jamba is the most suited to the dataset’s characteristics, there’s a risk that the model might not be the most effective for this task.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | N3l7aqN7DB | official_review | 1,731,336,445,370 | 1tVpbWoJRX | [
"everyone"
] | [
"~Ziyu_Zhao6"
] | title: Review
review: Overview:
This proposal presents a project aimed at improving financial market forecasting through a novel hybrid model called Jamba—a Mamba-Transformer model optimized for handling high-dimensional, sequential data in real-time. Leveraging recent advancements in machine learning, particularly in time-series prediction, this project participates in Jane Street’s Kaggle competition to enhance trading strategy models.
Strengths:
1. Innovative Model Choice: The use of a hybrid Mamba-Transformer model is an intriguing choice, as it combines the temporal dependency-capturing strengths of Transformers with the sequential data capabilities of Mamba. This is well-suited for the dynamic, high-dimensional data seen in financial markets.
2. Clear Methodology: The structured steps—from data preprocessing to evaluation—offer a clear roadmap for implementation, which can help in achieving reproducible results. The use of cross-validation and established error metrics like R2, MAE, and MSE is a solid approach to ensure reliability.
Weaknesses:
1. Potential Data Limitations: By relying solely on the provided Kaggle dataset, the project might miss out on additional data sources that could enrich training and generalization. Exploring supplementary datasets could further improve the model’s adaptability to diverse financial conditions.
2. Risk of Overfitting: Given the competitive context and focus on optimizing R2 for the Kaggle score, there is a risk of overfitting to the dataset and scoring metric. This focus could limit the model’s generalizability to unseen financial data, so regularization strategies or further evaluation metrics might be beneficial.
rating: 7
confidence: 3 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | IjKMSSnXak | official_review | 1,731,315,763,864 | 1tVpbWoJRX | [
"everyone"
] | [
"~Wenjing_Wu1"
] | title: Review
review: **Summary**:
The proposal outlines a Mamba-Transformer hybrid model designed to handle high-dimensional and sequential data
**Strengths**:
- Innovative Model Design: Combining Mamba and Transformer architectures could enhance temporal pattern recognition and adaptability to real-time data.
- Clear, Structured Methodology: The plan’s structure provides a comprehensive roadmap, from data preparation to evaluation metrics.
**Weaknesses**:
- Competition-based: Relying solely on the provided dataset may not be able to verify the capability of newly-proposed method, which would restrict the impact of it.
rating: 7
confidence: 3 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | BeieAFCm75 | official_review | 1,731,417,986,012 | 1tVpbWoJRX | [
"everyone"
] | [
"~jin_wang30"
] | title: Review
review: Overall, the article presents an innovative machine learning project proposal in the field of financial forecasting. The article explains the financial market background, data selection, model architecture innovation and target evaluation indicators at a high level, which illustrates the theoretical value and practical application potential of the model.
The article clearly organizes the content into sections such as "Background", "Definition", "Related Research" and "Proposed Method", making it easy for readers to understand how each link connects and supports the project goals. This structure is particularly suitable for complex machine learning project proposals because it not only presents the background and motivation, but also briefly introduces the implementation process of the project. In addition, the article details the challenges of financial market forecasting in the "Background" section, including data noise, instability and other issues, and mentions the actual impact of forecasting. This provides a reasonable background for the subsequent proposed methods and highlights the practical application value of this project, such as improving trading strategies and providing investment decision support. In addition, the article extends the prediction problem of financial markets to a wider range of time series prediction problems, such as healthcare and climate science, emphasizing the universality of the method. The article details the scoring criteria (R2) of the competition and states that this will be used as the main evaluation metric of the model, while MAE and MSE may be used as auxiliary indicators. This variety of evaluation index design helps to measure model performance from different angles and ensure that the model meets high standards in terms of accuracy and error control.
However, the article also has shortcomings. The first shortcoming is that there are many format problems in the article, which does not meet the requirements of this assignment. The second shortcoming is that the article is too weak in innovation, and only uses the large model Jamba without proposing any ideas for improvement.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | BTY5ynEdT2 | official_review | 1,731,325,914,608 | 1tVpbWoJRX | [
"everyone"
] | [
"~Yida_Lu1"
] | title: A practical method of a competition
review: This study attempts to use Jamba, a newly proposed machine learning structure which is a hybrid of Transformer and Mamba, to make predictions on financial datas in a Kaggle competition. The task is well-defined and the method is clear. On the other hand, this study may be further strengthened by comparing with the traditional Transformer models to demonstrate the effectiveness of Jamba. And there is also space to explore some innovative methods beyond simply fine-tuning to achieve a better performance.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | 9xLf3SxJ0X | official_review | 1,731,086,077,815 | 1tVpbWoJRX | [
"everyone"
] | [
"~Yanchen_Wu1"
] | title: Classical Problem
review: This work hopes to achieve better predictions by improving the structure of Mamba-Transformers. The problem studied is the financial data on kaggle, which is a very classic problem. I hope the authors can develop some new methods to achieve better results.
rating: 7
confidence: 4 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | 9GcecMImEn | official_review | 1,731,049,954,484 | 1tVpbWoJRX | [
"everyone"
] | [
"~Peidong_Zhang1"
] | title: Strengths and limitations of proposal
review: This proposal outlines the development of the Jamba model, a novel hybrid Mamba-Transformer architecture, for stock market prediction in the Kaggle competition organized by Jane Street. The project aims to leverage Jamba’s ability to capture complex temporal dependencies and handle high-dimensional, real-time data, making it suitable for financial forecasting. However, the proposal does not adequately address potential overfitting issues or the computational complexity of combining these two architectures. The reliance solely on the Kaggle dataset without considering external datasets might also limit the model's robustness and generalizability across diverse market conditions. Finally, the absence of a detailed risk analysis or mitigation strategies for data noise and missing values could hinder the model's ability to handle real-world financial unpredictability effectively.
rating: 8
confidence: 3 |
1tVpbWoJRX | [Proposal-ML] ***** | [
"Christoffer Brevik",
"Duc Justin",
"Zheng Timothé"
] | This proposal does not contain an abstract | [
"Jamba",
"Time-series forecasting",
"Machine Learning",
"Mamba",
"Transformers",
"Stock market prediction",
"Jane Street"
] | https://openreview.net/pdf?id=1tVpbWoJRX | 8SPTGf6pyb | official_review | 1,731,141,448,858 | 1tVpbWoJRX | [
"everyone"
] | [
"~Yuanda_Zhang1"
] | title: Kaggle competition
review: The proposal outlines a project aimed at predicting stock market behavior using a novel Mamba-Transformer hybrid model named Jamba. The project is motivated by the challenges of accurately forecasting financial markets and aims to participate in a Kaggle competition organized by Jane Street to develop robust prediction models.
The proposal addresses a significant and relevant challenge in financial forecasting, which has real-world implications for trading strategies and investment decisions. However, it would benefit from a deeper exploration of the model's adaptability to financial data challenges and a more comprehensive validation strategy beyond competition performance.
rating: 7
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | xnam8oU0Q6 | official_review | 1,731,077,550,182 | 1eajzjMKeW | [
"everyone"
] | [
"~Ruitao_Jing1"
] | title: A Novel LLM-Driven Multi-Agent Robotics Frameworks in Human-Centric Environments
review: This paper presents a novel framework leveraging Large Language Models (LLMs) to enhance the capabilities of multi-agent robotic systems in executing complex tasks within human-centric environments. The framework aims to address the current deficiencies in embodied intelligent robots, particularly in dynamic real-time reasoning, fine physical actions, and identifying opportunities to seek human assistance. The practicality and research value of this work are commendable, as it tackles critical challenges in the field of robotics and human-robot interaction.
However, the proposal's clarity regarding its innovation over prior work is somewhat obscured. Specifically, the section on "LLM-Based Multi-Agent Systems" lacks a clear delineation of how the proposed framework differs from and improves upon existing methodologies. The paper would benefit from a more detailed exposition on the unique contributions it offers to the field.
Furthermore, the proposal is somewhat vague on the specific algorithms and methodologies that will be employed to address the identified challenges. Without a more concrete description of the approach, it is difficult to assess the feasibility of the framework. Additionally, the paper does not mention the datasets that will be used or the design philosophy behind the key modules, which are crucial for evaluating the practical implementation of the proposed system.
In conclusion, while the paper presents an intriguing concept with significant potential, it requires further elaboration on its innovative aspects and a more detailed description of its technical underpinnings. Addressing these gaps would greatly enhance the paper's contribution to the field and provide a clearer path for future research and development.
rating: 9
confidence: 3 |
Subsets and Splits