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Out-of-Distribution Detection with Negative Prompts | https://openreview.net/forum?id=nanyAujl6e | https://openreview.net/forum?id=nanyAujl6e | Jun Nie,Yonggang Zhang,Zhen Fang,Tongliang Liu,Bo Han,Xinmei Tian | ICLR 2024,Poster | Out-of-distribution (OOD) detection is indispensable for open-world machine learning models. Inspired by recent success in large pre-trained language-vision models, e.g., CLIP, advanced works have achieved impressive OOD detection results by matching the *similarity* between image features and features of learned prompts, i.e., positive prompts. However, existing works typically struggle with OOD samples having similar features with those of known classes. One straightforward approach is to introduce negative prompts to achieve a *dissimilarity* matching, which further assesses the anomaly level of image features by introducing the absence of specific features. Unfortunately, our experimental observations show that either employing a prompt like "not a photo of a" or learning a prompt to represent "not containing" fails to capture the dissimilarity for identifying OOD samples. The failure may be contributed to the diversity of negative features, i.e., tons of features could indicate features not belonging to a known class. To this end, we propose to learn a set of negative prompts for each class. The learned positive prompt (for all classes) and negative prompts (for each class) are leveraged to measure the similarity and dissimilarity in the feature space simultaneously, enabling more accurate detection of OOD samples. Extensive experiments are conducted on diverse OOD detection benchmarks, showing the effectiveness of our proposed method. | https://openreview.net/pdf/c81083ef0572e587108dcdbae6a070f455081ca5.pdf |
$\pi$2vec: Policy Representation with Successor Features | https://openreview.net/forum?id=o5Bqa4o5Mi | https://openreview.net/forum?id=o5Bqa4o5Mi | Gianluca Scarpellini,Ksenia Konyushkova,Claudio Fantacci,Thomas Paine,Yutian Chen,Misha Denil | ICLR 2024,Poster | This paper introduces $\pi$2vec, a method for representing black box policies as comparable feature vectors.
Our method combines the strengths of foundation models that serve as generic and powerful state representations and successor features that can model the future occurrence of the states for a policy.
$\pi$2vec represents the behavior of policies by capturing the statistics of the features from a pretrained model with the help of successor feature framework.
We focus on the offline setting where policies and their representations are trained on a fixed dataset of trajectories.
Finally, we employ linear regression on $\pi$2vec vector representations to predict the performance of held out policies.
The synergy of these techniques results in a method for efficient policy evaluation in resource constrained environments. | https://openreview.net/pdf/35eacee9c8fd3a84eae896684e2e43482f5f8ddc.pdf |
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition | https://openreview.net/forum?id=TVg6hlfsKa | https://openreview.net/forum?id=TVg6hlfsKa | Feng Lu,Lijun Zhang,Xiangyuan Lan,Shuting Dong,Yaowei Wang,Chun Yuan | ICLR 2024,Poster | Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recognition (VPR). Due to the inherent difference in training objectives and data between the tasks of model pre-training and VPR, how to bridge the gap and fully unleash the capability of pre-trained models for VPR is still a key issue to address. To this end, we propose a novel method to realize seamless adaptation of pre-trained models for VPR. Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method to achieve both global and local adaptation efficiently, in which only lightweight adapters are tuned without adjusting the pre-trained model. Besides, to guide effective adaptation, we propose a mutual nearest neighbor local feature loss, which ensures proper dense local features are produced for local matching and avoids time-consuming spatial verification in re-ranking. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time, and uses about only 3% retrieval runtime of the two-stage VPR methods with RANSAC-based spatial verification. It ranks 1st on the MSLS challenge leaderboard (at the time of submission). The code is released at https://github.com/Lu-Feng/SelaVPR. | https://openreview.net/pdf/0f33cfc8d460213bc0e4fbcd89a561aa872b475b.pdf |
FROSTER: Frozen CLIP is A Strong Teacher for Open-Vocabulary Action Recognition | https://openreview.net/forum?id=zYXFMeHRtO | https://openreview.net/forum?id=zYXFMeHRtO | Xiaohu Huang,Hao Zhou,Kun Yao,Kai Han | ICLR 2024,Poster | In this paper, we introduce \textbf{FROSTER}, an effective framework for open-vocabulary action recognition. The CLIP model has achieved remarkable success in a range of image-based tasks, benefiting from its strong generalization capability stemming from pretaining on massive image-text pairs. However, applying CLIP directly to the open-vocabulary action recognition task is challenging due to the absence of temporal information in CLIP's pretraining. Further, fine-tuning CLIP on action recognition datasets may lead to overfitting and hinder its generalizability, resulting in unsatisfactory results when dealing with unseen actions.
To address these issues, FROSTER employs a residual feature distillation approach to ensure that CLIP retains its generalization capability while effectively adapting to the action recognition task. Specifically, the residual feature distillation treats the frozen CLIP model as a teacher to maintain the generalizability exhibited by the original CLIP and supervises the feature learning for the extraction of video-specific features to bridge the gap between images and videos. Meanwhile, it uses a residual sub-network for feature distillation to reach a balance between the two distinct objectives of learning generalizable and video-specific features.
We extensively evaluate FROSTER on open-vocabulary action recognition benchmarks under both base-to-novel and cross-dataset settings. FROSTER consistently achieves state-of-the-art performance on all datasets across the board.
Project page: \url{https://visual-ai.github.io/froster}. | https://openreview.net/pdf/86b440cbc1cddae74b393392d55e1bf9a1cab356.pdf |
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach | https://openreview.net/forum?id=SKulT2VX9p | https://openreview.net/forum?id=SKulT2VX9p | Aoqi Zuo,Yiqing Li,Susan Wei,Mingming Gong | ICLR 2024,Poster | Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race. In recent years, causal inference methods have been increasingly used in fair machine learning to measure unfairness by causal effects. However, current methods assume that the true causal graph is given, which is often not true in real-world applications. To address this limitation, this paper proposes a framework for achieving causal fairness based on the notion of interventions when the true causal graph is partially known. The proposed approach involves modeling fair prediction using a Partially Directed Acyclic Graph (PDAG), specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. The PDAG is used to measure causal fairness, and a constrained optimization problem is formulated to balance between fairness and accuracy. Results on both simulated and real-world datasets demonstrate the effectiveness of this method. | https://openreview.net/pdf/736413d85272f00bf5d77a32f654504c9ddb5fda.pdf |
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World | https://openreview.net/forum?id=c2R7ajodcI | https://openreview.net/forum?id=c2R7ajodcI | Weiyun Wang,Min Shi,Qingyun Li,Wenhai Wang,Zhenhang Huang,Linjie Xing,Zhe Chen,Hao Li,Xizhou Zhu,Zhiguo Cao,Yushi Chen,Tong Lu,Jifeng Dai,Yu Qiao | ICLR 2024,Poster | We present the All-Seeing (AS) project: a large-scale dataset and model for recognizing and understanding everything in the open world.
Using a scalable data engine that incorporates human feedback and efficient models in the loop, we create a new dataset (AS-1B) with over 1.2 billion regions annotated with semantic tags, question-answering pairs, and detailed captions. It covers a wide range of 3.5 million common and rare concepts in the real world and has 132.2 billion tokens that describe the concepts and their attributes. Leveraging this new dataset, we develop the All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding. The model is trained with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance, including both region- and image-level retrieval, region recognition, captioning, and question-answering. We hope that this project can serve as a foundation for vision-language artificial general intelligence research. Code is available at https://github.com/OpenGVLab/all-seeing. | https://openreview.net/pdf/06ae59a79d7a403142aa9e43faac0d3a871974fd.pdf |
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis | https://openreview.net/forum?id=pw2ssoOTpo | https://openreview.net/forum?id=pw2ssoOTpo | Xiaoxiao Sun,Xingjian Leng,Zijian Wang,Yang Yang,Zi Huang,Liang Zheng | ICLR 2024,Poster | Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a testbed with out-of-distribution test sets that have broad coverage of environmental discrepancies. However, existing testbeds typically either have a small number of domains or are synthesized by image corruptions, hindering algorithm design that demonstrates real-world effectiveness. In this paper, we introduce CIFAR-10-Warehouse, consisting of 180 datasets collected by prompting image search engines and diffusion models in various ways. Generally sized between 300 and 8,000 images, the datasets contain natural images, cartoons, certain colors, or objects that do not naturally appear. With CIFAR-10-W, we aim to enhance the evaluation and deepen the understanding of two generalization tasks: domain generalization and model accuracy prediction in various out-of-distribution environments. We conduct extensive benchmarking and comparison experiments and show that CIFAR-10-W offers new and interesting insights inherent to these tasks. We also discuss other fields that would benefit from CIFAR-10-W. Data and code are available at https://sites.google.com/view/CIFAR-10-warehouse/. | https://openreview.net/pdf/5292b79dd4a5284e9897949c752644f7ef258902.pdf |
Task Planning for Visual Room Rearrangement under Partial Observability | https://openreview.net/forum?id=jJvXNpvOdM | https://openreview.net/forum?id=jJvXNpvOdM | Karan Mirakhor,Sourav Ghosh,Dipanjan Das,Brojeshwar Bhowmick | ICLR 2024,Poster | This paper presents a novel hierarchical task planner under partial observability
that empowers an embodied agent to use visual input to efficiently plan a sequence
of actions for simultaneous object search and rearrangement in an untidy room,
to achieve a desired tidy state. The paper introduces (i) a novel Search Network
that utilizes commonsense knowledge from large language models to find unseen
objects, (ii) a Deep RL network trained with proxy reward, along with (iii) a novel
graph-based state representation to produce a scalable and effective planner that
interleaves object search and rearrangement to minimize the number of steps taken
and overall traversal of the agent, as well as to resolve blocked goal and swap
cases, and (iv) a sample-efficient cluster-biased sampling for simultaneous training
of the proxy reward network along with the Deep RL network. Furthermore,
the paper presents new metrics and a benchmark dataset - RoPOR, to measure
the effectiveness of rearrangement planning. Experimental results show that our
method significantly outperforms the state-of-the-art rearrangement methods Weihs
et al. (2021a); Gadre et al. (2022); Sarch et al. (2022); Ghosh et al. (2022). | https://openreview.net/pdf/12382b91f39a182002d5412bce5637aec72fe8c3.pdf |
Parallelizing non-linear sequential models over the sequence length | https://openreview.net/forum?id=E34AlVLN0v | https://openreview.net/forum?id=E34AlVLN0v | Yi Heng Lim,Qi Zhu,Joshua Selfridge,Muhammad Firmansyah Kasim | ICLR 2024,Poster | Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature.
For many years this bottleneck has persisted, as many thought sequential models could not be parallelized.
We challenge this long-held belief with our parallel algorithm that accelerates GPU evaluation of sequential models by up to 3 orders of magnitude faster without compromising output accuracy.
The algorithm does not need any special structure in the sequential models' architecture, making it applicable to a wide range of architectures.
Using our method, training sequential models can be more than 10 times faster than the common sequential method without any meaningful difference in the training results.
Leveraging this accelerated training, we discovered the efficacy of the Gated Recurrent Unit in a long time series classification problem with 17k time samples.
By overcoming the training bottleneck, our work serves as the first step to unlock the potential of non-linear sequential models for long sequence problems. | https://openreview.net/pdf/767f2f8a456b12dbc3932524fb34b44d5dd4c72a.pdf |
Long-tailed Diffusion Models with Oriented Calibration | https://openreview.net/forum?id=NW2s5XXwXU | https://openreview.net/forum?id=NW2s5XXwXU | Tianjiao Zhang,Huangjie Zheng,Jiangchao Yao,Xiangfeng Wang,Mingyuan Zhou,Ya Zhang,Yanfeng Wang | ICLR 2024,Poster | Diffusion models are acclaimed for generating high-quality and diverse images. However, their performance notably degrades when trained on data with a long-tailed distribution. For long tail diffusion model generation, current works focus on the calibration and enhancement of the tail generation with head-tail knowledge transfer. The transfer process relies on the abundant diversity derived from the head class and, more significantly, the condition capacity of the model prediction. However, the dependency on the conditional model prediction to realize the knowledge transfer might exhibit bias during training, leading to unsatisfactory generation results and lack of robustness. Utilizing a Bayesian framework, we develop a weighted denoising score-matching technique for knowledge transfer directly from head to tail classes. Additionally, we incorporate a gating mechanism in the knowledge transfer process. We provide statistical analysis to validate this methodology, revealing that the effectiveness of such knowledge transfer depends on both label distribution and sample similarity, providing the insight to consider sample similarity when re-balancing the label proportion in training. We extensively evaluate our approach with experiments on multiple benchmark datasets, demonstrating its effectiveness and superior performance compared to existing methods. Code: \url{https://github.com/MediaBrain-SJTU/OC_LT}. | https://openreview.net/pdf/54f6a3c60a297e6a8dd48de50da3e6761f35793a.pdf |
A Simple Romance Between Multi-Exit Vision Transformer and Token Reduction | https://openreview.net/forum?id=gJeYtRuguR | https://openreview.net/forum?id=gJeYtRuguR | Dongyang Liu,Meina Kan,Shiguang Shan,Xilin CHEN | ICLR 2024,Poster | Vision Transformers (ViTs) are now flourishing in the computer vision area. Despite the remarkable success, ViTs suffer from high computational costs, which greatly hinder their practical usage. Token reduction, which identifies and discards unimportant tokens during forward propagation, has then been proposed to make ViTs more efficient. For token reduction methodologies, a scoring metric is essential to distinguish between important and unimportant tokens. The attention score from the $\mathrm{[CLS]}$ token, which takes the responsibility to aggregate useful information and form the final output, has been established by prior works as an advantageous choice. Nevertheless, whereas the task pressure is applied at the end of the whole model, token reduction generally starts from very early blocks. Given the long distance in between, in the early blocks, $\mathrm{[CLS]}$ token lacks the impetus to gather task-relevant information, causing somewhat arbitrary attention allocation. This phenomenon, in turn, degrades the reliability of token scoring and substantially compromises the effectiveness of token reduction. Inspired by advances in the domain of dynamic neural networks, in this paper, we introduce Multi-Exit Token Reduction (METR), a simple romance between multi-exit architecture and token reduction—two areas previously considered orthogonal. By injecting early task pressure via multi-exit loss, the $\mathrm{[CLS]}$ token is spurred to collect task-related information in even early blocks, thus bolstering the credibility of $\mathrm{[CLS]}$ attention as a token-scoring metric. Additionally, we employ self-distillation to further refine the quality of early supervision. Extensive experiments substantiate both the existence and effectiveness of the newfound chemistry. Comparative assessments also indicate that METR outperforms state-of-the-art token reduction methods on standard benchmarks, especially under aggressive reduction ratios. | https://openreview.net/pdf/30327689a80f1c2ff2b92ac2444ecddc2b0299d3.pdf |
Optimal Sample Complexity for Average Reward Markov Decision Processes | https://openreview.net/forum?id=jOm5p3q7c7 | https://openreview.net/forum?id=jOm5p3q7c7 | Shengbo Wang,Jose Blanchet,Peter Glynn | ICLR 2024,Poster | We resolve the open question regarding the sample complexity of policy learning for maximizing the long-run average reward associated with a uniformly ergodic Markov decision process (MDP), assuming a generative model. In this context, the existing literature provides a sample complexity upper bound of $\widetilde O(|S||A|t_{\text{mix}}^2 \epsilon^{-2})$ and a lower bound of $\Omega(|S||A|t_{\text{mix}} \epsilon^{-2})$. In these expressions, $|S|$ and $|A|$ denote the cardinalities of the state and action spaces respectively, $t_{\text{mix}}$ serves as a uniform upper limit for the total variation mixing times, and $\epsilon$ signifies the error tolerance. Therefore, a notable gap of $t_{\text{mix}}$ still remains to be bridged. Our primary contribution is the development of an estimator for the optimal policy of average reward MDPs with a sample complexity of $\widetilde O(|S||A|t_{\text{mix}}\epsilon^{-2})$. This marks the first algorithm and analysis to reach the literature's lower bound. Our new algorithm draws inspiration from ideas in Li et al. (2020), Jin \& Sidford (2021), and Wang et al. (2023). Additionally, we conduct numerical experiments to validate our theoretical findings. | https://openreview.net/pdf/de79763fb3e561410918ae69fc6785c8b884a75f.pdf |
Spatio-Temporal Approximation: A Training-Free SNN Conversion for Transformers | https://openreview.net/forum?id=XrunSYwoLr | https://openreview.net/forum?id=XrunSYwoLr | Yizhou Jiang,Kunlin Hu,Tianren Zhang,Haichuan Gao,Yuqian Liu,Ying Fang,Feng Chen | ICLR 2024,Poster | Spiking neural networks (SNNs) are energy-efficient and hold great potential for large-scale inference. Since training SNNs from scratch is costly and has limited performance, converting pretrained artificial neural networks (ANNs) to SNNs is an attractive approach that retains robust performance without additional training data and resources. However, while existing conversion methods work well on convolution networks, emerging Transformer models introduce unique mechanisms like self-attention and test-time normalization, leading to non-causal non-linear interactions unachievable by current SNNs. To address this, we approximate these operations in both temporal and spatial dimensions, thereby providing the first SNN conversion pipeline for Transformers. We propose \textit{Universal Group Operators} to approximate non-linear operations spatially and a \textit{Temporal-Corrective Self-Attention Layer} that approximates spike multiplications at inference through an estimation-correction approach. Our algorithm is implemented on a pretrained ViT-B/32 from CLIP, inheriting its zero-shot classification capabilities, while improving control over conversion losses. To our knowledge, this is the first direct training-free conversion of a pretrained Transformer to a purely event-driven SNN, promising for neuromorphic hardware deployment. | https://openreview.net/pdf/42f89370e3ee45c347dff05cf3068158d33960ef.pdf |
NfgTransformer: Equivariant Representation Learning for Normal-form Games | https://openreview.net/forum?id=4YESQqIys7 | https://openreview.net/forum?id=4YESQqIys7 | Siqi Liu,Luke Marris,Georgios Piliouras,Ian Gemp,Nicolas Heess | ICLR 2024,Poster | Normal-form games (NFGs) are the fundamental model of *strategic interaction*. We study their representation using neural networks. We describe the inherent equivariance of NFGs --- any permutation of strategies describes an equivalent game --- as well as the challenges this poses for representation learning. We then propose the NfgTransformer architecture that leverages this equivariance, leading to state-of-the-art performance in a range of game-theoretic tasks including equilibrium-solving, deviation gain estimation and ranking, with a common approach to NFG representation. We show that the resulting model is interpretable and versatile, paving the way towards deep learning systems capable of game-theoretic reasoning when interacting with humans and with each other. | https://openreview.net/pdf/a7f80a91104b5e454bee9969ca68f37ddd46a2e2.pdf |
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models | https://openreview.net/forum?id=pszewhybU9 | https://openreview.net/forum?id=pszewhybU9 | Keming Lu,Hongyi Yuan,Zheng Yuan,Runji Lin,Junyang Lin,Chuanqi Tan,Chang Zhou,Jingren Zhou | ICLR 2024,Poster | Pre-trained large language models (LLMs) can understand and align with human instructions by supervised fine-tuning (SFT).
It is commonly believed that diverse and complex SFT data are of the essence to enable good instruction-following abilities.
However, such diversity and complexity are obscure and lack quantitative analyses.
In this work, we propose InsTag, an open-set instruction tagging method, to identify semantics and intentions of human instructions by tags that provide access to definitions and quantified analyses of instruction diversity and complexity.
We obtain 6.6K fine-grained tags to describe instructions from popular open-sourced SFT datasets comprehensively.
We find that the abilities of aligned LLMs benefit from more diverse and complex instructions in SFT data.
Based on this observation, we propose a data sampling procedure based on InsTag, and select 6K diverse and complex samples from open-source datasets for SFT.
The resulting models, TagLM, outperform open-source models based on considerably larger SFT data evaluated by MT-Bench, echoing the importance of instruction diversity and complexity and the effectiveness of InsTag.
InsTag has robust potential to be extended to more applications beyond the data selection as it provides an effective way to analyze the distribution of instructions. | https://openreview.net/pdf/13b6f81d90b7b92dd0e671d40c7c56694397d580.pdf |
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations | https://openreview.net/forum?id=JewzobRhay | https://openreview.net/forum?id=JewzobRhay | Aleksandar Petrov,Philip Torr,Adel Bibi | ICLR 2024,Poster | Context-based fine-tuning methods, including prompting, in-context learning, soft prompting (also known as prompt tuning), and prefix-tuning, have gained popularity due to their ability to often match the performance of full fine-tuning with a fraction of the parameters. Despite their empirical successes, there is little theoretical understanding of how these techniques influence the internal computation of the model and their expressiveness limitations. We show that despite the continuous embedding space being more expressive than the discrete token space, soft-prompting and prefix-tuning are potentially less expressive than full fine-tuning, even with the same number of learnable parameters. Concretely, context-based fine-tuning cannot change the relative attention pattern over the content and can only bias the outputs of an attention layer in a fixed direction. This suggests that while techniques like prompting, in-context learning, soft prompting, and prefix-tuning can effectively elicit skills present in the pretrained model, they may not be able to learn novel tasks that require new attention patterns. | https://openreview.net/pdf/79fb662643e39a4625a8e36f711ded2b169ae3ad.pdf |
Understanding In-Context Learning from Repetitions | https://openreview.net/forum?id=bGGYcvw8mp | https://openreview.net/forum?id=bGGYcvw8mp | Jianhao Yan,Jin Xu,Chiyu Song,Chenming Wu,Yafu Li,Yue Zhang | ICLR 2024,Poster | This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs). Our work provides a novel perspective by examining in-context learning via the lens of surface repetitions. We quantitatively investigate the role of surface features in text generation, and empirically establish the existence of \emph{token co-occurrence reinforcement}, a principle that strengthens the relationship between two tokens based on their contextual co-occurrences. Furthermore, we find similar reinforcements lie behind the pretraining corpus, revealing the existence is due to LLMs' efforts to maximize the likelihood. By investigating the dual impacts of these features, our research illuminates the internal workings of in-context learning and expounds on the reasons for its failures. This paper provides an essential contribution to the understanding of in-context learning and its potential limitations, providing a fresh perspective on this exciting capability. | https://openreview.net/pdf/a1909d9fbc70162734c42ad43ea40ab3b9083f74.pdf |
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity | https://openreview.net/forum?id=ndCJeysCPe | https://openreview.net/forum?id=ndCJeysCPe | Hugo Cui,Florent Krzakala,Eric Vanden-Eijnden,Lenka Zdeborova | ICLR 2024,Poster | We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture. We provide a sharp end-to-end analysis of the problem. First, we provide a tight closed-form characterization of the learnt velocity field, when parametrized by a shallow denoising auto-encoder trained on a finite number $n$ of samples from the target distribution. Building on this analysis, we provide a sharp description of the corresponding generative flow, which pushes the base Gaussian density forward to an approximation of the target density. In particular, we provide closed-form formulae for the distance between the means of the generated mixture and the mean of the target mixture, which we show decays as $\Theta_n(\frac{1}{n})$. Finally, this rate is shown to be in fact Bayes-optimal. | https://openreview.net/pdf/ef1375c37afc567f7f065ea7d0fa8f54792c7a89.pdf |
Few-shot Hybrid Domain Adaptation of Image Generator | https://openreview.net/forum?id=FE2e8664Sl | https://openreview.net/forum?id=FE2e8664Sl | Hengjia Li,Yang Liu,Linxuan Xia,Yuqi Lin,Wenxiao Wang,Tu Zheng,Zheng Yang,Xiaohui Zhong,Xiaobo Ren,Xiaofei He | ICLR 2024,Poster | Can a pre-trained generator be adapted to the hybrid of multiple target domains and generate images with integrated attributes of them? In this work, we introduce a new task -- Few-shot $\textit{Hybrid Domain Adaptation}$ (HDA). Given a source generator and several target domains, HDA aims to acquire an adapted generator that preserves the integrated attributes of all target domains, without overriding the source domain's characteristics. Compared with $\textit{Domain Adaptation}$ (DA), HDA offers greater flexibility and versatility to adapt generators to more composite and expansive domains. Simultaneously, HDA also presents more challenges than DA as we have access only to images from individual target domains and lack authentic images from the hybrid domain. To address this issue, we introduce a discriminator-free framework that directly encodes different domains' images into well-separable subspaces. To achieve HDA, we propose a novel directional subspace loss comprised of a distance loss and a direction loss. Concretely, the distance loss blends the attributes of all target domains by reducing the distances from generated images to all target subspaces. The direction loss preserves the characteristics from the source domain by guiding the adaptation along the perpendicular to subspaces. Experiments show that our method can obtain numerous domain-specific attributes in a single adapted generator, which surpasses the baseline methods in semantic similarity, image fidelity, and cross-domain consistency. | https://openreview.net/pdf/73c068a31c64941e219d68b570c0d5fcf11e43b8.pdf |
Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds | https://openreview.net/forum?id=GWSIo2MzuH | https://openreview.net/forum?id=GWSIo2MzuH | Yuxin Dong,Tieliang Gong,Hong Chen,Shujian Yu,Chen Li | ICLR 2024,Poster | Information-theoretic generalization analysis has achieved astonishing success in characterizing the generalization capabilities of noisy and iterative learning algorithms. However, current advancements are mostly restricted to average-case scenarios and necessitate the stringent bounded loss assumption, leaving a gap with regard to computationally tractable PAC generalization analysis, especially for long-tailed loss distributions. In this paper, we bridge this gap by introducing a novel class of PAC bounds through leveraging loss entropies. These bounds simplify the computation of key information metrics in previous PAC information-theoretic bounds to one-dimensional variables, thereby enhancing computational tractability. Moreover, our data-independent bounds provide novel insights into the generalization behavior of the minimum error entropy criterion, while our data-dependent bounds improve over previous results by alleviating the bounded loss assumption under both leave-one-out and supersample settings. Extensive numerical studies indicate strong correlations between the generalization error and the induced loss entropy, showing that the presented bounds adeptly capture the patterns of the true generalization gap under various learning scenarios. | https://openreview.net/pdf/9eb371a8ebb47a35223daf716f911bc641d2662e.pdf |
Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation | https://openreview.net/forum?id=vePdNU3u6n | https://openreview.net/forum?id=vePdNU3u6n | Yaofo Chen,Shuaicheng Niu,Yaowei Wang,Shoukai Xu,Hengjie Song,Mingkui Tan | ICLR 2024,Poster | The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usually, the models shall remain fixed once deployed (at least for some period) due to the potential high cost of model adaptation for both the server and edge sides. However, in many real-world scenarios, the test environments may change dynamically (known as distribution shifts), which often results in degraded performance. Thus, one has to adapt the edge models promptly to attain promising performance. Moreover, with the increasing data collected at the edge, this paradigm also fails to further adapt the cloud model for better performance. To address these, we encounter two primary challenges: 1) the edge model has limited computation power and may only support forward propagation; 2) the data transmission budget between cloud and edge devices is limited in latency-sensitive scenarios. In this paper, we establish a Cloud-Edge Elastic Model Adaptation (CEMA) paradigm in which the edge models only need to perform forward propagation and the edge models can be adapted online. In our CEMA, to reduce the communication burden, we devise two criteria to exclude unnecessary samples from uploading to the cloud, i.e., dynamic unreliable and low-informative sample exclusion. Based on the uploaded samples, we update and distribute the affine parameters of normalization layers by distilling from the stronger foundation model to the edge model with a sample replay strategy. Extensive experimental results on ImageNet-C and ImageNet-R verify the effectiveness of our CEMA. | https://openreview.net/pdf/3424e1c5c434bf91fc5544a318ed5b4e7c69e576.pdf |
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval | https://openreview.net/forum?id=b3kDP3IytM | https://openreview.net/forum?id=b3kDP3IytM | Marah I Abdin,Suriya Gunasekar,Varun Chandrasekaran,Jerry Li,Mert Yuksekgonul,Rahee Ghosh Peshawaria,Ranjita Naik,Besmira Nushi | ICLR 2024,Poster | We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., “a list of ice cream shops in San Diego”). In the past, such queries were considered as tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent abilities in this task. However, many current retrieval benchmarks are either saturated or do not measure constraint satisfaction. Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models. KITAB consists of book-related data across more than 600 authors and 13,000 queries, and also offers an associated dynamic data collection and constraint verification approach for acquiring similar test data for other authors. Our extended experiments on GPT4 and GPT3.5 characterize and decouple common failure modes across dimensions such as information popularity, constraint types, and context availability. Results show that in the absence of context, models exhibit severe limitations as measured by irrelevant information, factual errors, and incompleteness, many of which exacerbate as information popularity decreases. While context availability mitigates irrelevant information, it is not helpful for satisfying constraints, identifying fundamental barriers to constraint satisfaction. We open source our contributions to foster further research on improving constraint satisfaction abilities of future models. | https://openreview.net/pdf/9e65b0b2ea12f3c79f0d33fcb1d62101e1a0faa1.pdf |
Boosting Graph Anomaly Detection with Adaptive Message Passing | https://openreview.net/forum?id=CanomFZssu | https://openreview.net/forum?id=CanomFZssu | Jingyan Chen,Guanghui Zhu,Chunfeng Yuan,Yihua Huang | ICLR 2024,Poster | Unsupervised graph anomaly detection has been widely used in real-world applications. Existing methods primarily focus on local inconsistency mining (LIM), based on the intuition that establishing high similarities between abnormal nodes and their neighbors is difficult. However, the message passing employed by graph neural networks (GNNs) results in local anomaly signal loss, as GNNs tend to make connected nodes similar, which conflicts with the LIM intuition. In this paper, we propose GADAM, a novel framework that not only resolves the conflict between LIM and message passing but also leverages message passing to augment anomaly detection through a transformative approach to anomaly mining beyond LIM. Specifically, we first propose an efficient MLP-based LIM approach to obtain local anomaly scores in a conflict-free way. Next, we introduce a novel approach to capture anomaly signals from a global perspective. This involves a hybrid attention based adaptive message passing, enabling nodes to selectively absorb abnormal or normal signals from their surroundings. Extensive experiments conducted on nine benchmark datasets, including two large-scale OGB datasets, demonstrate that GADAM surpassinges existing state-of-the-art methods in terms of both effectiveness and efficiency. | https://openreview.net/pdf/577aad3134e8ddd9309792f26abae173da9778c4.pdf |
MINDE: Mutual Information Neural Diffusion Estimation | https://openreview.net/forum?id=0kWd8SJq8d | https://openreview.net/forum?id=0kWd8SJq8d | Giulio Franzese,Mustapha BOUNOUA,Pietro Michiardi | ICLR 2024,Poster | In this work we present a new method for the estimation of Mutual Information (MI) between random variables. Our approach is based on an original interpretation of the Girsanov theorem, which allows us to use score-based diffusion models to estimate the KL divergence between two densities as a difference between their score functions. As a by-product, our method also enables the estimation of the entropy of random variables.
Armed with such building blocks, we present a general recipe to measure MI, which unfolds in two directions: one uses conditional diffusion process, whereas the other uses joint diffusion processes that allow simultaneous modelling of two random variables.
Our results, which derive from a thorough experimental protocol over all the variants of our approach, indicate that our method is more accurate than the main alternatives from the literature, especially for challenging distributions. Furthermore, our methods pass MI self-consistency tests, including data processing and additivity under independence, which instead are a pain-point of existing methods | https://openreview.net/pdf/d63ef2086a523427ae0cbc8da8dc34a62c1348a8.pdf |
Continual Momentum Filtering on Parameter Space for Online Test-time Adaptation | https://openreview.net/forum?id=BllUWdpIOA | https://openreview.net/forum?id=BllUWdpIOA | Jae-Hong Lee,Joon-Hyuk Chang | ICLR 2024,Poster | Deep neural networks (DNNs) have revolutionized tasks such as image classification and speech recognition but often falter when training and test data diverge in distribution. External factors, from weather effects on images to varied speech environments, can cause this discrepancy, compromising DNN performance. Online test-time adaptation (OTTA) methods present a promising solution, recalibrating models in real-time during the test stage without requiring historical data. However, the OTTA paradigm is imperfect, often falling prey to issues such as catastrophic forgetting due to its reliance on noisy, self-trained predictions. Although some contemporary strategies mitigate this by tying adaptations to the static source model, this restricts model flexibility. This paper introduces a continual momentum filtering (CMF) framework, leveraging the Kalman filter (KF) to strike a balance between model adaptability and information retention. The CMF intertwines optimization via stochastic gradient descent with a KF-based inference process. This methodology not only aids in averting catastrophic forgetting but also provides high adaptability to shifting data distributions. We validate our framework on various OTTA scenarios and real-world situations regarding covariate and label shifts, and the CMF consistently shows superior performance compared to state-of-the-art methods. | https://openreview.net/pdf/9e82322464300d482134ba6dc6d8b73f52a05976.pdf |
Deep Reinforcement Learning for Modelling Protein Complexes | https://openreview.net/forum?id=4MsfQ2H0lP | https://openreview.net/forum?id=4MsfQ2H0lP | Ziqi Gao,Tao Feng,Jiaxuan You,Chenyi Zi,Yan Zhou,Chen Zhang,Jia Li | ICLR 2024,Poster | Structure prediction of large protein complexes (a.k.a., protein multimer mod-
elling, PMM) can be achieved through the one-by-one assembly using provided
dimer structures and predicted docking paths. However, existing PMM methods
struggle with vast search spaces and generalization challenges: (1) The assembly
of a N -chain multimer can be depicted using graph structured data, with each
chain represented as a node and assembly actions as edges. Thus the assembly
graph can be arbitrary acyclic undirected connected graph, leading to the com-
binatorial optimization space of N^(N −2) for the PMM problem. (2) Knowledge
transfer in the PMM task is non-trivial. The gradually limited data availability as
the chain number increases necessitates PMM models that can generalize across
multimers of various chains. To address these challenges, we propose GAPN, a
Generative Adversarial Policy Network powered by domain-specific rewards and
adversarial loss through policy gradient for automatic PMM prediction. Specifi-
cally, GAPN learns to efficiently search through the immense assembly space and
optimize the direct docking reward through policy gradient. Importantly, we de-
sign a adversarial reward function to enhance the receptive field of our model. In
this way, GAPN will simultaneously focus on a specific batch of multimers and
the global assembly rules learned from multimers with varying chain numbers.
Empirically, we have achieved both significant accuracy (measured by RMSD
and TM-Score) and efficiency improvements compared to leading complex mod-
eling software. GAPN outperforms the state-of-the-art method (MoLPC) with up
to 27% improvement in TM-Score, with a speed-up of 600×. | https://openreview.net/pdf/6c7994999a1ec0d262ce4871a1dee819500dcfe2.pdf |
fairret: a Framework for Differentiable Fairness Regularization Terms | https://openreview.net/forum?id=NnyD0Rjx2B | https://openreview.net/forum?id=NnyD0Rjx2B | Maarten Buyl,MaryBeth Defrance,Tijl De Bie | ICLR 2024,Poster | Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines.
We introduce a framework of fairness regularization terms (fairret) which quantify bias as modular objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework. | https://openreview.net/pdf/2d46d65c27b06135211c83e38334c352424133eb.pdf |
Debiasing Algorithm through Model Adaptation | https://openreview.net/forum?id=XIZEFyVGC9 | https://openreview.net/forum?id=XIZEFyVGC9 | Tomasz Limisiewicz,David Mareček,Tomáš Musil | ICLR 2024,Poster | Large language models are becoming the go-to solution for the ever-growing number of tasks.
However, with growing capacity, models are prone to rely on spurious correlations stemming from biases and stereotypes present in the training data.
This work proposes a novel method for detecting and mitigating gender bias in language models.
We perform causal analysis to identify problematic model components and discover that mid-upper feed-forward layers are most prone to convey bias.
Based on the analysis results, we intervene in the model by applying a linear projection to the weight matrices of these layers.
Our titular method DAMA, significantly decreases bias as measured by diverse metrics while maintaining the model's performance on downstream tasks.
We release code for our method and models, which retrain LLaMA's state-of-the-art performance while being significantly less biased. | https://openreview.net/pdf/3e34a39b782c4e457844fca76050c5ae73e41062.pdf |
A Foundation Model for Error Correction Codes | https://openreview.net/forum?id=7KDuQPrAF3 | https://openreview.net/forum?id=7KDuQPrAF3 | Yoni Choukroun,Lior Wolf | ICLR 2024,Poster | In recent years, Artificial Intelligence has undergone a paradigm shift with the rise of foundation models, which are trained on large amounts of data, typically in a self-supervised way, and can then be adapted to a wide range of downstream tasks. In this work, we propose the first foundation model for Error Correction Codes. This model is trained on multiple codes and can then be applied to an unseen code. To enable this, we extend the Transformer architecture in multiple ways: (1) a code-invariant initial embedding, which is also position- and length-invariant, (2) a learned modulation of the attention maps that is conditioned on the Tanner graph, and (3) a length-invariant code-aware noise prediction module that is based on the parity-check matrix. The proposed architecture is trained on multiple short- and medium-length codes and is able to generalize to unseen codes. Its performance on these codes matches and even outperforms the state of the art, despite having a smaller capacity than the leading code-specific transformers. The suggested framework therefore demonstrates, for the first time, the benefits of learning a universal decoder rather than a neural decoder optimized for a given code. | https://openreview.net/pdf/abb1891d4cc668f9ad11b7924405b7cb25b2cc7d.pdf |
Seer: Language Instructed Video Prediction with Latent Diffusion Models | https://openreview.net/forum?id=qHGgNyQk31 | https://openreview.net/forum?id=qHGgNyQk31 | Xianfan Gu,Chuan Wen,Weirui Ye,Jiaming Song,Yang Gao | ICLR 2024,Poster | Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning.
To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named Seer, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We enhance the U-Net and language conditioning model by incorporating computation-efficient spatial-temporal attention. Furthermore, we introduce a novel Frame Sequential Text Decomposer module that dissects a sentence's global instruction into temporally aligned sub-instructions, ensuring precise integration into each frame of generation. Our framework allows us to effectively leverage the extensive prior knowledge embedded in pretrained T2I models across the frames.
With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2), Bridgedata and EpicKitchens-100 datasets demonstrate our superior video prediction performance with around 480-GPU hours versus CogVideo with over 12,480-GPU hours: achieving the 31\% FVD improvement compared to the current SOTA model on SSv2 and 83.7\% average preference in the human evaluation. Our project is available at https://seervideodiffusion.github.io/ | https://openreview.net/pdf/b4ad5effde78b04f3efda6933cc9586927c29004.pdf |
Matrix Manifold Neural Networks++ | https://openreview.net/forum?id=30aSE3FB3L | https://openreview.net/forum?id=30aSE3FB3L | Xuan Son Nguyen,Shuo Yang,Aymeric Histace | ICLR 2024,Poster | Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and hyperbolic manifolds have the rich algebraic structures of gyrogroups and gyrovector spaces. This enables principled and effective generalizations of the most successful DNNs to these manifolds. Recently, some works have shown that many concepts in the theory of gyrogroups and gyrovector spaces can also be generalized to matrix manifolds such as Symmetric Positive Definite (SPD) and Grassmann manifolds. As a result, some building blocks for SPD and Grassmann neural networks, e.g., isometric models and multinomial logistic regression (MLR) can be derived in a way that is fully analogous to their spherical and hyperbolic counterparts. Building upon these works, in this paper, we design fully-connected (FC) and convolutional layers for SPD neural networks. We also develop MLR on Symmetric Positive Semi-definite (SPSD) manifolds, and propose a method for performing backpropagation with the Grassmann logarithmic map in the projector perspective. We demonstrate the effectiveness of the proposed approach in the human action recognition and node classification tasks. | https://openreview.net/pdf/4e1eb1720c744ca0ed64c8e9dbbda53036ce0ba5.pdf |
EMO: EARTH MOVER DISTANCE OPTIMIZATION FOR AUTO-REGRESSIVE LANGUAGE MODELING | https://openreview.net/forum?id=4bLXfRd0CX | https://openreview.net/forum?id=4bLXfRd0CX | Siyu Ren,Zhiyong Wu,Kenny Q. Zhu | ICLR 2024,Poster | Neural language models are probabilistic models of human text. They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forward cross-entropy between the empirical data distribution and the model distribution. However, various degeneration phenomena are still widely observed when decoding from the distributions learned by such models. We establish that the forward cross-entropy is suboptimal as a distance metric for aligning human and model distribution due to its (1) recall-prioritization (2) negative diversity ignorance and (3) train-test mismatch. In this paper, we propose Earth Mover Distance Optimization (EMO) for auto-regressive language modeling. EMO capitalizes on the inherent properties of earth mover distance to address the aforementioned challenges. Due to the high complexity of direct computation, we further introduce a feasible upper bound for EMO to ease end-to-end training. Upon extensive evaluation of language models trained using EMO and MLE. We find that EMO demonstrates a consistently better language modeling performance than MLE across domains. Moreover, EMO demonstrates noteworthy enhancements in downstream performance with minimal fine-tuning on merely 25,000 sentences. This highlights the tremendous potential of EMO as a lightweight calibration method for enhancing large-scale pre-trained language models. | https://openreview.net/pdf/6b921e5f84e5257651d95fd8540cd1d7cd603186.pdf |
Are Human-generated Demonstrations Necessary for In-context Learning? | https://openreview.net/forum?id=frRDT6EOhg | https://openreview.net/forum?id=frRDT6EOhg | Rui Li,Guoyin Wang,Jiwei Li | ICLR 2024,Poster | Despite the promising few-shot ability of large language models (LLMs), the standard paradigm of In-context Learning (ICL) suffers the disadvantages of susceptibility to selected demonstrations and the intricacy to generate these demonstrations. In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL. To answer this question, we propose self-contemplation prompting strategy (SEC), a paradigm free from human-crafted demonstrations. The key point of SEC is that, instead of using hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create demonstrations on their own, based on which the final output is generated. SEC is a flexible framework and can be adapted to both the vanilla ICL and the chain-of-thought (CoT), but with greater ease: as the manual-generation process of both examples and rationale can be saved. Extensive experiments in arithmetic reasoning, commonsense reasoning, multi-task language understanding, and code generation benchmarks, show that SEC, which does not require hand-crafted demonstrations, significantly outperforms the zero-shot learning strategy, and achieves comparable results to ICL with hand-crafted demonstrations. This demonstrates that, for many tasks, contemporary LLMs possess a sufficient level of competence to exclusively depend on their own capacity for decision making, removing the need for external training data. | https://openreview.net/pdf/859ab4076ff5dd86d77a47c0cdd7acedcce0b30d.pdf |
LLM-Assisted Code Cleaning For Training Accurate Code Generators | https://openreview.net/forum?id=maRYffiUpI | https://openreview.net/forum?id=maRYffiUpI | Naman Jain,Tianjun Zhang,Wei-Lin Chiang,Joseph E. Gonzalez,Koushik Sen,Ion Stoica | ICLR 2024,Poster | Natural language to code generation is an important application area of LLMs and has received wide attention from the community.
The majority of relevant studies have exclusively concentrated on increasing the quantity and functional correctness of training sets while disregarding other stylistic elements of programs. More recently, data quality has garnered a lot of interest and multiple works have showcased its importance for improving performance. In this work, we investigate data quality for code and find that making the code more structured and readable leads to improved code generation performance of the system. We build a novel data-cleaning pipeline that uses these principles to transform existing programs by 1.) renaming variables, 2.) modularizing and decomposing complex code into smaller helper sub-functions, and 3.) inserting natural-language based planning annotations. We evaluate our approach on two challenging algorithmic code generation benchmarks and find that fine-tuning CodeLLaMa-7B on our transformed programs improves the performance by up to \textbf{30\%} compared to fine-tuning on the original dataset. Additionally, we demonstrate improved performance from using a smaller amount of higher-quality data, finding that a model fine-tuned on the entire original dataset is outperformed by a model trained on one-eighth of our cleaned dataset. Even in comparison to closed-source models, our models outperform the much larger AlphaCode models. | https://openreview.net/pdf/abdd4c0aa83ef59ad6f792d31a4ba8cecb383198.pdf |
HYPO: Hyperspherical Out-Of-Distribution Generalization | https://openreview.net/forum?id=VXak3CZZGC | https://openreview.net/forum?id=VXak3CZZGC | Haoyue Bai,Yifei Ming,Julian Katz-Samuels,Yixuan Li | ICLR 2024,Poster | Out-of-distribution (OOD) generalization is critical for machine learning models deployed in the real world. However, achieving this can be fundamentally challenging, as it requires the ability to learn invariant features across different domains or environments. In this paper, we propose a novel framework HYPO (HYPerspherical OOD generalization) that provably learns domain-invariant representations in a hyperspherical space. In particular, our hyperspherical learning algorithm is guided by intra-class variation and inter-class separation principles—ensuring that features from the same class (across different training domains) are closely aligned with their class prototypes, while different class prototypes are maximally separated. We further provide theoretical justifications on how our prototypical learning objective improves the OOD generalization bound. Through extensive experiments on challenging OOD benchmarks, we demonstrate that our approach outperforms competitive baselines and achieves superior performance. Code is available at https://github.com/deeplearning-wisc/hypo. | https://openreview.net/pdf/6507b7dc5e58c3d5b846684c1a7e0ea2c52b2096.pdf |
Analyzing and Improving Optimal-Transport-based Adversarial Networks | https://openreview.net/forum?id=jODehvtTDx | https://openreview.net/forum?id=jODehvtTDx | Jaemoo Choi,Jaewoong Choi,Myungjoo Kang | ICLR 2024,Poster | Optimal Transport (OT) problem aims to find a transport plan that bridges two distributions while minimizing a given cost function. OT theory has been widely utilized in generative modeling. In the beginning, OT distance has been used as a measure for assessing the distance between data and generated distributions. Recently, OT transport map between data and prior distributions has been utilized as a generative model. These OT-based generative models share a similar adversarial training objective. In this paper, we begin by unifying these OT-based adversarial methods within a single framework. Then, we elucidate the role of each component in training dynamics through a comprehensive analysis of this unified framework. Moreover, we suggest a simple but novel method that improves the previously best-performing OT-based model. Intuitively, our approach conducts a gradual refinement of the generated distribution, progressively aligning it with the data distribution. Our approach achieves a FID score of 2.51 on CIFAR-10 and 5.99 on CelebA-HQ-256, outperforming unified OT-based adversarial approaches. | https://openreview.net/pdf/ab22d7148780987b465f0ed7fc393e645136178f.pdf |
SEABO: A Simple Search-Based Method for Offline Imitation Learning | https://openreview.net/forum?id=MNyOI3C7YB | https://openreview.net/forum?id=MNyOI3C7YB | Jiafei Lyu,Xiaoteng Ma,Le Wan,Runze Liu,Xiu Li,Zongqing Lu | ICLR 2024,Poster | Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment. Nevertheless, the success of offline RL relies heavily on the offline transitions annotated with reward labels. In practice, we often need to hand-craft the reward function, which is sometimes difficult, labor-intensive, or inefficient. To tackle this challenge, we set our focus on the offline imitation learning (IL) setting, and aim at getting a reward function based on the expert data and unlabeled data. To that end, we propose a simple yet effective search-based offline IL method, tagged SEABO. SEABO allocates a larger reward to the transition that is close to its closest neighbor in the expert demonstration, and a smaller reward otherwise, all in an unsupervised learning manner. Experimental results on a variety of D4RL datasets indicate that SEABO can achieve competitive performance to offline RL algorithms with ground-truth rewards, given only a single expert trajectory, and can outperform prior reward learning and offline IL methods across many tasks. Moreover, we demonstrate that SEABO also works well if the expert demonstrations contain only observations. Our code is publicly available at https://github.com/dmksjfl/SEABO. | https://openreview.net/pdf/8623a11c82d4d56bfa3ba1d11b5337ba469cf4c6.pdf |
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs | https://openreview.net/forum?id=1ndDmZdT4g | https://openreview.net/forum?id=1ndDmZdT4g | Yuxin Zhang,Lirui Zhao,Mingbao Lin,Sun Yunyun,Yiwu Yao,Xingjia Han,Jared Tanner,Shiwei Liu,Rongrong Ji | ICLR 2024,Poster | The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre-LLMs approaches in reducing model complexity, network pruning appears to lag behind in the era of LLMs, due mostly to its costly fine-tuning (or re-training) necessity under the massive volumes of model parameter and training data. To close this industry-academia gap, we introduce Dynamic Sparse No Training ($\texttt{DSNT}$), a training-free fine-tuning approach that slightly updates sparse LLMs without the expensive backpropagation and any weight updates. Inspired by the Dynamic Sparse Training, $\texttt{DSNT}$ minimizes the reconstruction error between the dense and sparse LLMs, in the fashion of performing iterative weight pruning-and-growing on top of sparse LLMs. To accomplish this purpose, $\texttt{DSNT}$ particularly takes into account the anticipated reduction in reconstruction error for pruning and growing, as well as the variance w.r.t. different input data for growing each weight. This practice can be executed efficiently in linear time since its obviates the need of backpropagation for fine-tuning LLMs. Extensive experiments on LLaMA-V1/V2, Vicuna, and OPT across various benchmarks demonstrate the effectiveness of $\texttt{DSNT}$ in enhancing the performance of sparse LLMs, especially at high sparsity levels. For instance, $\texttt{DSNT}$ is able to outperform the state-of-the-art Wanda by 26.79 perplexity at 70% sparsity with LLaMA-7B. Our paper offers fresh insights into how to fine-tune sparse LLMs in an efficient training-free manner and open new venues to scale the great potential of sparsity to LLMs. Codes are available at https://github.com/zyxxmu/DSnoT. | https://openreview.net/pdf/c8a19471ce8d7538229d3403c6c2d7e43122c60d.pdf |
Simplifying Transformer Blocks | https://openreview.net/forum?id=RtDok9eS3s | https://openreview.net/forum?id=RtDok9eS3s | Bobby He,Thomas Hofmann | ICLR 2024,Poster | A simple design recipe for deep Transformers is to compose identical building blocks. But standard transformer blocks are far from simple, interweaving attention and MLP sub-blocks with skip connections \& normalisation layers in precise arrangements. This complexity leads to brittle architectures, where seemingly minor changes can significantly reduce training speed, or render models untrainable.
In this work, we ask to what extent the standard transformer block can be simplified? Combining signal propagation theory and empirical observations, we motivate modifications that allow many block components to be removed with no loss of training speed, including skip connections, projection or value parameters, sequential sub-blocks and normalisation layers. In experiments on both autoregressive decoder-only and BERT encoder-only models, our simplified transformers match the per-iteration training speed and performance of standard transformers, while enjoying 16\% faster training throughput, and using 15\% fewer parameters. | https://openreview.net/pdf/5c0bc332f56f507c2f176689f84cb4353e9c0851.pdf |
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost | https://openreview.net/forum?id=cINwAhrgLf | https://openreview.net/forum?id=cINwAhrgLf | Yuan Gao,WEIZHONG ZHANG,Wenhan Luo,Lin Ma,Jin-Gang Yu,Gui-Song Xia,Jiayi Ma | ICLR 2024,Poster | We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task inference cost of the primary task. While most existing auxiliary learning methods are optimization-based relying on loss weights/gradients manipulation, our method is architecture-based with a flexible asymmetric structure for the primary and auxiliary tasks, which produces different networks for training and inference. Specifically, starting from two single task networks/branches (each representing a task), we propose a novel method with evolving networks where only primary-to-auxiliary links exist as the cross-task connections after convergence. These connections can be removed during the primary task inference, resulting in a single-task inference cost. We achieve this by formulating a Neural Architecture Search (NAS) problem, where we initialize bi-directional connections in the search space and guide the NAS optimization converging to an architecture with only the single-side primary-to-auxiliary connections. Moreover, our method can be incorporated with optimization-based auxiliary learning approaches. Extensive experiments with six tasks on NYU v2, CityScapes, and Taskonomy datasets using VGG, ResNet, and ViT backbones validate the promising performance. The codes are available at https://github.com/ethanygao/Aux-NAS. | https://openreview.net/pdf/5f009b99c187d83bc92692bc20b98869293ccbaf.pdf |
EX-Graph: A Pioneering Dataset Bridging Ethereum and X | https://openreview.net/forum?id=juE0rWGCJW | https://openreview.net/forum?id=juE0rWGCJW | Qian Wang,Zhen Zhang,Zemin Liu,Shengliang Lu,Bingqiao Luo,Bingsheng He | ICLR 2024,Poster | While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data. This constraint limits the incorporation of relevant social network data into blockchain analysis, thereby diminishing the breadth and depth of insight that can be derived. To address the above limitation, we introduce EX-Graph, a novel dataset that authentically links Ethereum and X, marking the first and largest dataset of its kind. EX-Graph combines Ethereum transaction records (2 million nodes and 30 million edges) and X following data (1 million nodes and 3 million edges), bonding 30,667 Ethereum addresses with verified X accounts sourced from OpenSea. Detailed statistical analysis on EX- Graph highlights the structural differences between X-matched and non-X-matched Ethereum addresses. Extensive experiments, including Ethereum link prediction, wash-trading Ethereum addresses detection, and X-Ethereum matching link pre- diction, emphasize the significant role of X data in enhancing Ethereum analysis. EX-Graph is available at https://exgraph.deno.dev/. | https://openreview.net/pdf/904db34098e4829427ee4107309125c0d511bf4e.pdf |
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting | https://openreview.net/forum?id=tm8s3696Ox | https://openreview.net/forum?id=tm8s3696Ox | Rong Dai,Yonggang Zhang,Ang Li,Tongliang Liu,Xun Yang,Bo Han | ICLR 2024,Poster | One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server model is aggregated by distilling knowledge from all client models (the ensemble), which are also responsible for synthesizing samples for distillation. In this regard, advanced works show that the performance of the server model is intrinsically related to the quality of the synthesized data and the ensemble model. To promote OFL, we introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively. Specifically, Co-Boosting leverages the current ensemble model to synthesize higher-quality samples in an adversarial attack manner. These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model. Consequently, Co-Boosting periodically achieves high-quality data and ensemble models. Extensive experiments demonstrate that Co-Boosting can substantially outperform existing baselines under various settings. Moreover, Co-Boosting eliminates the need for adjustments to the client's local training, requires no additional data or model transmission, and allows client models to have heterogeneous architectures. | https://openreview.net/pdf/1d94c1c4591570933f7c6c739f8f9f677d0f4b6d.pdf |
Symbol as Points: Panoptic Symbol Spotting via Point-based Representation | https://openreview.net/forum?id=aOnUe8ah7j | https://openreview.net/forum?id=aOnUe8ah7j | WENLONG LIU,Tianyu Yang,Yuhan Wang,Qizhi Yu,Lei Zhang | ICLR 2024,Poster | This work studies the problem of panoptic symbol spotting, which is to spot and parse both countable object instances (windows, doors, tables, etc.) and uncountable stuff (wall, railing, etc.) from computer-aided design (CAD) drawings. Existing methods typically involve either rasterizing the vector graphics into images and using image-based methods for symbol spotting, or directly building graphs and using graph neural networks for symbol recognition. In this paper, we take a different approach, which treats graphic primitives as a set of 2D points that are locally connected and use point cloud segmentation methods to tackle it. Specifically, we utilize a point transformer to extract the primitive features and append a mask2former-like spotting head to predict the final output. To better use the local connection information of primitives and enhance their discriminability, we further propose the attention with connection module (ACM) and contrastive connection learning scheme (CCL). Finally, we propose a KNN interpolation mechanism for the mask attention module of the spotting head to better handle primitive mask downsampling, which is primitive-level in contrast to pixel-level for the image. Our approach, named SymPoint, is simple yet effective, outperforming recent state-of-the-art method GAT-CADNet by an absolute increase of 9.6% PQ and 10.4% RQ on the FloorPlanCAD dataset. The source code and models will be available at \url{https://github.com/nicehuster/SymPoint}. | https://openreview.net/pdf/9e5ddb4c15f06c798fa181f68e3defb8e3ea7729.pdf |
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs | https://openreview.net/forum?id=DZUzOKE6og | https://openreview.net/forum?id=DZUzOKE6og | Sunwoo Kim,Shinhwan Kang,Fanchen Bu,Soo Yong Lee,Jaemin Yoo,Kijung Shin | ICLR 2024,Poster | Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances in generative self-supervised learning (SSL) suggest that hypergraph neural networks (HNNs) learned from generative self-supervision have the potential to effectively encode the complex hypergraph topology. Designing a generative SSL strategy for hypergraphs, however, is not straightforward. Questions remain with regard to its generative SSL task, connection to downstream tasks, and empirical properties of learned representations. In light of the promises and challenges, we propose a novel generative SSL strategy for hypergraphs. We first formulate a generative SSL task on hypergraphs, hyperedge filling, and highlight its theoretical connection to node classification. Based on the generative SSL task, we propose a hypergraph SSL method, HYPEBOY. HYPEBOY learns effective general-purpose hypergraph representations, outperforming 15 baseline methods across 11 benchmark datasets. To our knowledge, this is the first study on generative SSL on hypergraphs, and we demonstrate its theoretical and empirical strengths for hypergraph representation learning. | https://openreview.net/pdf/8164a92cf5a77d8045eeab86de58c96ebbcb088f.pdf |
Zero-Shot Robustification of Zero-Shot Models | https://openreview.net/forum?id=fCeUoDr9Tq | https://openreview.net/forum?id=fCeUoDr9Tq | Dyah Adila,Changho Shin,Linrong Cai,Frederic Sala | ICLR 2024,Poster | Zero-shot inference is a powerful paradigm that enables the use of large pretrained models for downstream classification tasks without further training. However, these models are vulnerable to inherited biases that can impact their performance. The traditional solution is fine-tuning, but this undermines the key advantage of pretrained models, which is their ability to be used out-of-the-box. We propose RoboShot, a method that improves the robustness of pretrained model embeddings in a fully zero-shot fashion. First, we use language models (LMs) to obtain useful insights from task descriptions. These insights are embedded and used to remove harmful and boost useful components in embeddings---without any supervision. Theoretically, we provide a simple and tractable model for biases in zero-shot embeddings and give a result characterizing under what conditions our approach can boost performance. Empirically, we evaluate RoboShot on nine image and NLP classification tasks and show an average improvement of 15.98% over several zero-shot baselines. Additionally, we demonstrate that RoboShot is compatible with a variety of pretrained and language models and propose a way to further boost performance with a zero-shot adaptation variant. | https://openreview.net/pdf/a9c896245ba1138726d7063bbbea26cb2233b149.pdf |
THOUGHT PROPAGATION: AN ANALOGICAL APPROACH TO COMPLEX REASONING WITH LARGE LANGUAGE MODELS | https://openreview.net/forum?id=SBoRhRCzM3 | https://openreview.net/forum?id=SBoRhRCzM3 | Junchi Yu,Ran He,Zhitao Ying | ICLR 2024,Poster | Large Language Models (LLMs) have achieved remarkable success in reasoning tasks with the development of prompting methods.
However, existing prompting approaches cannot reuse insights of solving similar problems and suffer from accumulated errors in multi-step reasoning, since they prompt LLMs to reason \textit{from scratch}.
To address these issues, we propose \textbf{\textit{Thought Propagation} (TP)}, which explores the analogous problems and leverages their solutions to enhance the complex reasoning ability of LLMs.
These analogous problems are related to the input one, with reusable solutions and problem-solving strategies.
Thus, it is promising to propagate insights of solving previous analogous problems to inspire new problem-solving.
To achieve this, TP first prompts LLMs to propose and solve a set of analogous problems that are related to the input one.
Then, TP reuses the results of analogous problems to directly yield a new solution or derive a knowledge-intensive plan for execution to amend the initial solution obtained from scratch.
TP is compatible with existing prompting approaches, allowing plug-and-play generalization and enhancement in a wide range of tasks without much labor in task-specific prompt engineering.
Experiments across three challenging tasks demonstrate TP enjoys a substantial improvement over the baselines by an average of 12\% absolute increase in finding the optimal solutions in Shortest-path Reasoning, 13\% improvement of human preference in Creative Writing, and 15\% enhancement in the task completion rate of LLM-Agent Planning. | https://openreview.net/pdf/1cddcaa3f523f7ad1c9aea87d5180f2d173e4bb8.pdf |
FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction | https://openreview.net/forum?id=82Mc5ilInM | https://openreview.net/forum?id=82Mc5ilInM | Yuxing Tian,Yiyan Qi,Fan Guo | ICLR 2024,Poster | Link prediction is a crucial task in dynamic graph learning. Recent advancements in continuous-time dynamic graph models, primarily by leveraging richer temporal details, have significantly improved link prediction performance. However, due to their complex modules, they still face several challenges, such as overfitting and optimization difficulties. More importantly, it is challenging for these methods to capture the 'shift' phenomenon, where node interaction patterns change over time. To address these issues, we propose a simple yet novel method called \textbf{Fre}quency \textbf{E}nhanced Continuous-Time \textbf{Dy}namic \textbf{G}raph ({\bf FreeDyG}) model for link prediction. Specifically, we propose a node interaction frequency encoding module that both explicitly captures the proportion of common neighbors and the frequency of the interaction of the node pair. Unlike previous works that primarily focus on the time domain, we delve into the frequency domain, allowing a deeper and more nuanced extraction of interaction patterns, revealing periodic and "shift" behaviors. Extensive experiments conducted on seven real-world continuous-time dynamic graph datasets validate the effectiveness of FreeDyG. The results consistently demonstrate that FreeDyG outperforms existing methods in both transductive and inductive settings. Our code is available at this repository: \href{https://github.com/Tianxzzz/FreeDyG}{https://github.com/Tianxzzz/FreeDyG} | https://openreview.net/pdf/7c102abd54a02926bd7a15ae53c31d21235d4834.pdf |
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing | https://openreview.net/forum?id=GruDNzQ4ux | https://openreview.net/forum?id=GruDNzQ4ux | Vint Lee,Pieter Abbeel,Youngwoon Lee | ICLR 2024,Poster | Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex behaviors in a sample-efficient way: planning actions by generating imaginary trajectories with predicted rewards. Despite its success, we found that surprisingly, reward prediction is often a bottleneck of MBRL, especially for sparse rewards that are challenging (or even ambiguous) to predict. Motivated by the intuition that humans can learn from rough reward estimates, we propose a simple yet effective reward smoothing approach, DreamSmooth, which learns to predict a temporally-smoothed reward, instead of the exact reward at the given timestep. We empirically show that DreamSmooth achieves state-of-the-art performance on long-horizon sparse-reward tasks both in sample efficiency and final performance without losing performance on common benchmarks, such as Deepmind Control Suite and Atari benchmarks. | https://openreview.net/pdf/f013e28c639c77fd41de92b839fc3d565566b8a3.pdf |
VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE | https://openreview.net/forum?id=qCyhvr0GG8 | https://openreview.net/forum?id=qCyhvr0GG8 | Haonan Yu,Wei Xu | ICLR 2024,Poster | Unsupervised video object learning seeks to decompose video scenes into structural object representations without any supervision from depth, optical flow, or segmentation. We present VONet, an innovative approach that is inspired by MONet. While utilizing a U-Net architecture, VONet employs an efficient and effective parallel attention inference process, generating attention masks for all slots simultaneously. Additionally, to enhance the temporal consistency of each mask across consecutive video frames, VONet develops an object-wise sequential VAE framework. The integration of these innovative encoder-side techniques, in conjunction with an expressive transformer-based decoder, establishes VONet as the leading unsupervised method for object learning across five MOVI datasets, encompassing videos of diverse complexities. Code is available at https://github.com/hnyu/vonet. | https://openreview.net/pdf/5b82cda9ad850c7fc16901ecc5256575699349e3.pdf |
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation | https://openreview.net/forum?id=1d2cLKeNgY | https://openreview.net/forum?id=1d2cLKeNgY | Bo Zhang,Xinyu Cai,Jiakang Yuan,Donglin Yang,Jianfei Guo,Xiangchao Yan,Renqiu Xia,Botian Shi,Min Dou,Tao Chen,Si Liu,Junchi Yan,Yu Qiao | ICLR 2024,Poster | Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training. Code and simulated points are available at: https://github.com/PJLab-ADG/3DTrans | https://openreview.net/pdf/254e0f6f9ac41817ec1e4f4ec69c767f6b847994.pdf |
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE | https://openreview.net/forum?id=rTDyN8yajn | https://openreview.net/forum?id=rTDyN8yajn | Zeren Chen,Ziqin Wang,Zhen Wang,Huayang Liu,Zhenfei Yin,Si Liu,Lu Sheng,Wanli Ouyang,Jing Shao | ICLR 2024,Poster | Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more modalities and downstream tasks are introduced, negative conflicts and interference may have a worse impact on performance. While this phenomenon has been overlooked in previous work, we propose a novel and extensible framework, called Octavius, for comprehensive studies and experimentation on multimodal learning with Multimodal Large Language Models (MLLMs). Specifically, to mitigate the interference, we combine the concept of Mixture-of-Experts (MoE) with LoRA and design a multimodal LoRA-MoE decoder for task- and modality-specific learning. To the best of our knowledge, we are one of the pioneering efforts to introduce MoE into MLLMs to address this problem. The experimental results (about 20% improvement) have shown the effectiveness and versatility of our design in various 2D and 3D downstream tasks. Code and corresponding dataset will be available
soon. | https://openreview.net/pdf/6a9c47592496b605392d6c26d8c05e65ddc8066e.pdf |
Motion Guidance: Diffusion-Based Image Editing with Differentiable Motion Estimators | https://openreview.net/forum?id=WIAO4vbnNV | https://openreview.net/forum?id=WIAO4vbnNV | Daniel Geng,Andrew Owens | ICLR 2024,Poster | Diffusion models are capable of generating impressive images conditioned on text descriptions, and extensions of these models allow users to edit images at a relatively coarse scale. However, the ability to precisely edit the layout, position, pose, and shape of objects in images with diffusion models is still difficult. To this end, we propose _motion guidance_, a zero-shot technique that allows a user to specify dense, complex motion fields that indicate where each pixel in an image should move. Motion guidance works by steering the diffusion sampling process with the gradients through an off-the-shelf optical flow network. Specifically, we design a guidance loss that encourages the sample to have the desired motion, as estimated by a flow network, while also being visually similar to the source image. By simultaneously sampling from a diffusion model and guiding the sample to have low guidance loss, we can obtain a motion-edited image. We demonstrate that our technique works on complex motions and produces high quality edits of real and generated images. | https://openreview.net/pdf/306d735a2ceebb3ced2641e98e7e8e294dedcf57.pdf |
Balancing Act: Constraining Disparate Impact in Sparse Models | https://openreview.net/forum?id=Xz13DtbOVW | https://openreview.net/forum?id=Xz13DtbOVW | Meraj Hashemizadeh,Juan Ramirez,Rohan Sukumaran,Golnoosh Farnadi,Simon Lacoste-Julien,Jose Gallego-Posada | ICLR 2024,Poster | Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that _directly addresses the disparate impact of pruning_: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups. | https://openreview.net/pdf/f35eeca085e541733536c8036bc48912b95f3787.pdf |
NECO: NEural Collapse Based Out-of-distribution detection | https://openreview.net/forum?id=9ROuKblmi7 | https://openreview.net/forum?id=9ROuKblmi7 | Mouïn Ben Ammar,Nacim Belkhir,Sebastian Popescu,Antoine Manzanera,Gianni Franchi | ICLR 2024,Poster | Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that "neural collapse", a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we introduce NECO, a novel post-hoc method for OOD detection, which leverages the geometric properties of “neural collapse” and of principal component spaces to identify OOD data. Our extensive experiments demonstrate that NECO achieves state-of-the-art results on both small and large-scale OOD detection tasks while exhibiting strong generalization capabilities across different network architectures. Furthermore, we provide a theoretical explanation for the effectiveness of our method in OOD detection. We plan to release the code after the anonymity period. | https://openreview.net/pdf/b3a8919014a86f33291a00f60a093d15953f1332.pdf |
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference | https://openreview.net/forum?id=lHasEfGsXL | https://openreview.net/forum?id=lHasEfGsXL | Yifan Feng,Yihe Luo,Shihui Ying,Yue Gao | ICLR 2024,Poster | Hypergraph Neural Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling.
However, it is noticed that the high-order modeling capability of hypergraph also brings increased computation complexity, which hinders its practical industrial deployment.
In practice, we find that one key barrier to the efficient deployment of HGNNs is the high-order structural dependencies during inference.
In this paper, we propose to bridge the gap between the HGNNs and inference-efficient Multi-Layer Perceptron (MLPs) to eliminate the hypergraph dependency of HGNNs and thus reduce computational complexity as well as improve inference speed.
Specifically, we introduce LightHGNN and LightHGNN$^+$ for fast inference with low complexity. LightHGNN directly distills the knowledge from teacher HGNNs to student MLPs via soft labels, and LightHGNN$^+$ further explicitly injects reliable high-order correlations into the student MLPs to achieve topology-aware distillation and resistance to over-smoothing.
Experiments on eight hypergraph datasets demonstrate that even without hypergraph dependency, the proposed LightHGNNs can still achieve competitive or even better performance than HGNNs and outperform vanilla MLPs by $16.3$ on average. Extensive experiments on three graph datasets further show the average best performance of our LightHGNNs compared with all other methods.
Experiments on synthetic hypergraphs with 5.5w vertices indicate LightHGNNs can run $100\times$ faster than HGNNs, showcasing their ability for latency-sensitive deployments. | https://openreview.net/pdf/a56c441a5107b922f1268eb91721d0b5ddb37f0c.pdf |
Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments | https://openreview.net/forum?id=HC0msxE3sf | https://openreview.net/forum?id=HC0msxE3sf | Ryo Ueda,Tadahiro Taniguchi | ICLR 2024,Poster | As a sub-discipline of evolutionary and computational linguistics, emergent communication (EC) studies communication protocols, called emergent languages, arising in simulations where agents communicate. A key goal of EC is to give rise to languages that share statistical properties with natural languages. In this paper, we reinterpret Lewis's signaling game, a frequently used setting in EC, as beta-VAE and reformulate its objective function as ELBO. Consequently, we clarify the existence of prior distributions of emergent languages and show that the choice of the priors can influence their statistical properties. Specifically, we address the properties of word lengths and segmentation, known as Zipf's law of abbreviation (ZLA) and Harris's articulation scheme (HAS), respectively. It has been reported that the emergent languages do not follow them when using the conventional objective. We experimentally demonstrate that by selecting an appropriate prior distribution, more natural segments emerge, while suggesting that the conventional one prevents the languages from following ZLA and HAS. | https://openreview.net/pdf/78d32d44c18244751d93a2c4d05b5e835d625d15.pdf |
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts | https://openreview.net/forum?id=I4wB3HA3dJ | https://openreview.net/forum?id=I4wB3HA3dJ | Ruipeng Zhang,Ziqing Fan,Jiangchao Yao,Ya Zhang,Yanfeng Wang | ICLR 2024,Poster | This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of SAM across different domains, which induces optimization bias towards certain domains and thus impairs the overall convergence. To address this issue, we consider the domain-level convergence consistency in the sharpness estimation to prevent the overwhelming (deficient) perturbations for less (well) optimized domains. Specifically, DISAM introduces the constraint of minimizing variance in the domain loss, which allows the elastic gradient calibration in perturbation generation: when one domain is optimized above the averaging level w.r.t. loss, the gradient perturbation towards that domain will be weakened automatically, and vice versa. Under this mechanism, we theoretically show that DISAM can achieve faster overall convergence and improved generalization in principle when inconsistent convergence emerges. Extensive experiments on various domain generalization benchmarks show the superiority of DISAM over a range of state-of-the-art methods. Furthermore, we show the superior efficiency of DISAM in parameter-efficient fine-tuning combined with the pretraining models. The source code is released at https://github.com/MediaBrain-SJTU/DISAM. | https://openreview.net/pdf/44663352a0f145c68eb0d984b79f69c717be57c9.pdf |
Making LLaMA SEE and Draw with SEED Tokenizer | https://openreview.net/forum?id=0Nui91LBQS | https://openreview.net/forum?id=0Nui91LBQS | Yuying Ge,Sijie Zhao,Ziyun Zeng,Yixiao Ge,Chen Li,Xintao Wang,Ying Shan | ICLR 2024,Poster | The great success of Large Language Models (LLMs) has expanded the potential of multimodality, contributing to the gradual evolution of General Artificial Intelligence (AGI). A true AGI agent should not only possess the capability to perform predefined multi-tasks but also exhibit emergent abilities in an open-world context. However, despite the considerable advancements made by recent multimodal LLMs, they still fall short in effectively unifying comprehension and generation tasks, let alone open-world emergent abilities. We contend that the key to overcoming the present impasse lies in enabling text and images to be represented and processed interchangeably within a unified autoregressive Transformer. To this end, we introduce $\textbf{SEED}$, an elaborate image tokenizer that empowers LLMs with the ability to $\textbf{SEE}$ and $\textbf{D}$raw at the same time. We identify two crucial design principles: (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a $\textit{1D causal dependency}$, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs. (2) Image tokens should capture $\textit{high-level semantics}$ consistent with the degree of semantic abstraction in words, and be optimized for both discriminativeness and reconstruction during the tokenizer training phase. With SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe, i.e., next-word prediction. SEED-LLaMA is therefore produced by large-scale pretraining and instruction tuning on the interleaved textual and visual data, demonstrating impressive performance on a broad range of multimodal comprehension and generation tasks. More importantly, SEED-LLaMA has exhibited compositional emergent abilities such as multi-turn in-context multimodal generation, acting like your AI assistant. The code (training and inference) and models are released in https://github.com/AILab-CVC/SEED. | https://openreview.net/pdf/9cec65274a4089a6a0a5d6ff2b5b72ea832de748.pdf |
A Cognitive Model for Learning Abstract Relational Structures from Memory-based Decision-Making Tasks | https://openreview.net/forum?id=KC58bVmxyN | https://openreview.net/forum?id=KC58bVmxyN | Haruo Hosoya | ICLR 2024,Poster | Motivated by a recent neuroscientific hypothesis, some theoretical studies have accounted for neural cognitive maps in the rodent hippocampal formation as a representation of the general relational structure across task environments. However, despite their remarkable results, it is unclear whether their account can be extended to more general settings beyond spatial random-walk tasks in 2D environments. To address this question, we construct a novel cognitive model that performs memory-based relational decision-making tasks, inspired by previous human studies, for learning abstract structures in non-spatial relations. Building on previous approaches of modular architecture, we develop a learning algorithm that performs reward-guided search for representation of abstract relations, while dynamically maintaining their binding to concrete entities using our specific memory mechanism enabling content replacement. Our experiments show (i) the capability of our model to capture relational structures that can generalize over new domains with unseen entities, (ii) the difficulty of our task that leads previous models, including Neural Turing Machine and vanilla Transformer, to complete failure, and (iii) the similarity of performance and internal representations of our model to recent human behavioral and fMRI experimental data in the human hippocampal formation. | https://openreview.net/pdf/248fbfe1b0797fdbf103cb619e90fa5f850337b6.pdf |
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization | https://openreview.net/forum?id=koYsgfEwCQ | https://openreview.net/forum?id=koYsgfEwCQ | Yanpeng Zhao,Siyu Gao,Yunbo Wang,Xiaokang Yang | ICLR 2024,Poster | Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaVol, a 3D scene generative model that unifies geometric structures and object-centric learning in a differentiable volume rendering framework. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene, which infers the probability distribution over objects at individual spatial locations. These voxel features evolve over time through a canonical-space deformation function, forming the basis for global representation learning via slot attention. The voxel features and global features are complementary and are both leveraged by a compositional NeRF decoder for volume rendering. DynaVol remarkably outperforms existing approaches for unsupervised dynamic scene decomposition. Once trained, the explicitly meaningful voxel features enable additional capabilities that 2D scene decomposition methods cannot achieve: it is possible to freely edit the geometric shapes or manipulate the motion trajectories of the objects. | https://openreview.net/pdf/af405f9d46b4fc807c78df621a71cc8971a946f8.pdf |
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series | https://openreview.net/forum?id=Tuh4nZVb0g | https://openreview.net/forum?id=Tuh4nZVb0g | Chenxi Sun,Hongyan Li,Yaliang Li,Shenda Hong | ICLR 2024,Poster | This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large model, or fine-tunes a pre-trained LLM for TS data; TS-for-LLM (data-centric) converts TS into a model-friendly representation to enable the pre-trained LLM to handle TS data. Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM. The proposed method is named TEST. It first tokenizes TS, builds an encoder to embed TS via instance-wise, feature-wise, and text-prototype-aligned contrast, where the TS embedding space is aligned to LLM’s embedding layer space, then creates soft prompts to make LLM more open to that embeddings, and finally implements TS tasks using the frozen LLM. We also demonstrate the feasibility of TS-for-LLM through theory and experiments. Experiments are carried out on TS classification, forecasting, and representation tasks using eight frozen LLMs with various structures and sizes. The results show that the pre-trained LLM with TEST strategy can achieve better or comparable performance than today's SOTA TS models, and offers benefits for few-shot and generalization. By treating LLM as the pattern machine, TEST can endow LLM's ability to process TS data without compromising language ability. We hope that this study will serve as a foundation for future work to support TS+LLM progress. | https://openreview.net/pdf/fe6b80833c7f27af0d32c91910a9a2e503677f6b.pdf |
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape | https://openreview.net/forum?id=U0IOMStUQ8 | https://openreview.net/forum?id=U0IOMStUQ8 | Rundi Wu,Ruoshi Liu,Carl Vondrick,Changxi Zheng | ICLR 2024,Poster | Synthesizing novel 3D models that resemble the input example as long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusion model that learns the internal patch distribution from a single 3D textured shape
and generates high-quality variations with fine geometry and texture details. Training a diffusion model directly in 3D would induce large memory and computational cost. Therefore, we first compress the input into a lower-dimensional latent space and then train a diffusion model on it. Specifically, we encode the input 3D textured shape into triplane feature maps that represent the signed distance and texture fields of the input. The denoising network of our diffusion model has a limited receptive field to avoid overfitting, and uses triplane-aware 2D convolution blocks to improve the result quality. Aside from randomly generating new samples, our model also facilitates applications such as retargeting, outpainting and local editing. Through extensive qualitative and quantitative evaluation, we show that our method outperforms prior methods in generation quality of 3D shapes. | https://openreview.net/pdf/7aedd975f4e9a43204c03b09f8746861d0ac2be3.pdf |
Pooling Image Datasets with Multiple Covariate Shift and Imbalance | https://openreview.net/forum?id=2Mo7v69otj | https://openreview.net/forum?id=2Mo7v69otj | Sotirios Panagiotis Chytas,Vishnu Suresh Lokhande,Vikas Singh | ICLR 2024,Poster | Small sample sizes are common in many disciplines,
which necessitates pooling roughly similar datasets across
multiple sites/institutions to study weak but relevant
associations between images and disease incidence. Such
data often manifest shifts and imbalances in covariates
(secondary non-imaging data).
These issues are well-studied for classical models, but
the ideas simply do not apply to overparameterized DNN models.
Consequently, recent work has shown how strategies from
fairness and invariant representation learning provides
a meaningful starting point, but the current repertoire
of methods remains limited to accounting for shifts/imbalances in just a couple of covariates at a time. In this paper, we show how
viewing this problem from the perspective of Category theory
provides a simple and effective solution that completely avoids
elaborate multi-stage training pipelines that would otherwise be
needed. We show the effectiveness of this approach via
extensive experiments on real datasets. Further, we
discuss how our style of formulation offers a unified
perspective on at least 5+ distinct
problem settings in vision, from self-supervised learning
to matching problems in 3D reconstruction. | https://openreview.net/pdf/9957791cb675731ede0cc1879c39291382c375f8.pdf |
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes | https://openreview.net/forum?id=3zKtaqxLhW | https://openreview.net/forum?id=3zKtaqxLhW | Rishabh Agarwal,Nino Vieillard,Yongchao Zhou,Piotr Stanczyk,Sabela Ramos Garea,Matthieu Geist,Olivier Bachem | ICLR 2024,Poster | Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, current KD methods for auto-regressive sequence models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference. To address this issue, we introduce Generalized Knowledge Distillation (GKD). Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences. Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when the student lacks the expressivity to mimic the teacher's distribution. Furthermore, GKD facilitates the seamless integration of distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for distilling auto-regressive T5 language models on summarization, translation, and arithmetic reasoning tasks. | https://openreview.net/pdf/abd6995c6b42062f68aeb7ca69eafbfd223a4951.pdf |
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation | https://openreview.net/forum?id=apXtolxDaJ | https://openreview.net/forum?id=apXtolxDaJ | Qiang He,Tianyi Zhou,Meng Fang,Setareh Maghsudi | ICLR 2024,Poster | Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performance. Hence, fine-tuning representation rank presents a challenging and crucial optimization problem. To address this issue, we find a guiding principle for adaptive control of the representation rank. We employ the Bellman equation as a theoretical foundation and derive an upper bound on the cosine similarity of consecutive state-action pairs representations of value networks. We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER). This regularizer adaptively regularizes the representation rank, thus improving the DRL agent's performance. We first validate the effectiveness of automatic control of rank on illustrative experiments. Then, we scale up BEER to complex continuous control tasks by combining it with the deterministic policy gradient method. Among 12 challenging DeepMind control tasks, BEER outperforms the baselines by a large margin. Besides, BEER demonstrates significant advantages in Q-value approximation. Our code is available at https://github.com/sweetice/BEER-ICLR2024. | https://openreview.net/pdf/adfaea6830ef33e1030928938bb4d8aef895d266.pdf |
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks | https://openreview.net/forum?id=ZL6yd6N1S2 | https://openreview.net/forum?id=ZL6yd6N1S2 | Puja Trivedi,Mark Heimann,Rushil Anirudh,Danai Koutra,Jayaraman J. Thiagarajan | ICLR 2024,Poster | While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while post-hoc calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better intrinsic uncertainty estimates are particularly valuable, as they can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the functional diversity induced by our anchoring strategies when sampling hypotheses renders this unnecessary and allows us to support G-$\Delta$UQ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on the uncertainty-based tasks of out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates. | https://openreview.net/pdf/bea2dc6c6603b053b1ec962f0b7d73a845b85111.pdf |
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation | https://openreview.net/forum?id=MIEnYtlGyv | https://openreview.net/forum?id=MIEnYtlGyv | Ameya Daigavane,Song Eun Kim,Mario Geiger,Tess Smidt | ICLR 2024,Poster | We present Symphony, an $E(3)$ equivariant autoregressive generative model for 3D molecular geometries
that iteratively builds a molecule from molecular fragments.
Existing autoregressive models such as G-SchNet and G-SphereNet for molecules utilize rotationally invariant features to respect the 3D symmetries of molecules.
In contrast, Symphony uses message-passing with higher-degree $E(3)$-equivariant features.
This allows a novel representation of probability distributions via spherical harmonic signals to efficiently model the 3D geometry of
molecules. We show that Symphony is able to accurately generate small molecules from the QM9 dataset, outperforming existing
autoregressive models and approaching the performance of diffusion models. Our code is available at https://github.com/atomicarchitects/symphony. | https://openreview.net/pdf/45fac35396fb78bd48275db319bdfa397d5b945e.pdf |
Set Learning for Accurate and Calibrated Models | https://openreview.net/forum?id=HZ3S17EI0o | https://openreview.net/forum?id=HZ3S17EI0o | Lukas Muttenthaler,Robert A. Vandermeulen,Qiuyi Zhang,Thomas Unterthiner,Klaus Robert Muller | ICLR 2024,Poster | Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a novel method to alleviate these problems that we call odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets rather than for single examples. This naturally allows the model to capture correlations across data examples and achieves both better accuracy and calibration, especially in limited training data and class-imbalanced regimes. Perhaps surprisingly, OKO often yields better calibration even when training with hard labels and dropping any additional calibration parameter tuning, such as temperature scaling. We demonstrate this in extensive experimental analyses and provide a mathematical theory to interpret our findings. We emphasize that OKO is a general framework that can be easily adapted to many settings and a trained model can be applied to single examples at inference time, without significant run-time overhead or architecture changes. | https://openreview.net/pdf/adfa290924866508a30f9b956ebd476eac36ae31.pdf |
INViTE: INterpret and Control Vision-Language Models with Text Explanations | https://openreview.net/forum?id=5iENGLEJKG | https://openreview.net/forum?id=5iENGLEJKG | Haozhe Chen,Junfeng Yang,Carl Vondrick,Chengzhi Mao | ICLR 2024,Poster | Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the underlying rules behind these models’ predictions and controlling model behaviors have remained open challenges. We present INViTE: a framework for INterpreting Vision Transformer’s latent tokens with Text Explanations. Given a latent token, INViTE retains its semantic information to the final layer using transformer’s local operations and retrieves the closest text for explanation. INViTE enables understanding of model visual reasoning procedure without needing additional model training or data collection. Based on the obtained interpretations, INViTE allows for model editing that controls model reasoning behaviors and improves model robustness against biases and spurious correlations. Our code is available at https://github.com/tonychenxyz/vit-interpret. | https://openreview.net/pdf/2020d6c177387f93d785a67aa668f7b0a75f6496.pdf |
Trajeglish: Traffic Modeling as Next-Token Prediction | https://openreview.net/forum?id=Z59Rb5bPPP | https://openreview.net/forum?id=Z59Rb5bPPP | Jonah Philion,Xue Bin Peng,Sanja Fidler | ICLR 2024,Poster | A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Using a simple data-driven tokenization scheme, we discretize trajectories to centimeter-level resolution using a small vocabulary. We then model the multi-agent sequence of discrete motion tokens with a GPT-like encoder-decoder that is autoregressive in time and takes into account intra-timestep interaction between agents. Scenarios sampled from our model exhibit state-of-the-art realism; our model tops the Waymo Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%. We ablate our modeling choices in full autonomy and partial autonomy settings, and show that the representations learned by our model can quickly be adapted to improve performance on nuScenes. We additionally evaluate the scalability of our model with respect to parameter count and dataset size, and use density estimates from our model to quantify the saliency of context length and intra-timestep interaction for the traffic modeling task. | https://openreview.net/pdf/6a1754e5443464338e361380403b4fec2f5d94b2.pdf |
Meaning Representations from Trajectories in Autoregressive Models | https://openreview.net/forum?id=UyGWafcopT | https://openreview.net/forum?id=UyGWafcopT | Tian Yu Liu,Matthew Trager,Alessandro Achille,Pramuditha Perera,Luca Zancato,Stefano Soatto | ICLR 2024,Poster | We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text. This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model. Moreover, unlike vector-based representations, distribution-based representations can also model asymmetric relations (e.g., direction of logical entailment, hypernym/hyponym relations) by using algebraic operations between likelihood functions. These ideas are grounded in distributional perspectives on semantics and are connected to standard constructions in automata theory, but to our knowledge they have not been applied to modern language models. We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle. Finally, we extend our method to represent data from different modalities (e.g., image and text) using multimodal autoregressive models. Our code is available at: https://github.com/tianyu139/meaning-as-trajectories | https://openreview.net/pdf/7b0c96b0676b9a4c87008b8719d7e36c9049f57e.pdf |
SWAP: Sparse Entropic Wasserstein Regression for Robust Network Pruning | https://openreview.net/forum?id=LJWizuuBUy | https://openreview.net/forum?id=LJWizuuBUy | Lei You,Hei Victor Cheng | ICLR 2024,Poster | This study addresses the challenge of inaccurate gradients in computing the empirical Fisher Information Matrix during network pruning. We introduce SWAP, a formulation of Entropic Wasserstein regression (EWR) for network pruning, capitalizing on the geometric properties of the optimal transport problem. The “swap” of the commonly used linear regression with the EWR in optimization is analytically demonstrated to offer noise mitigation effects by incorporating neighborhood interpolation across data points with only marginal additional computational cost. The unique strength of SWAP is its intrinsic ability to balance noise reduction and covariance information preservation effectively. Extensive experiments performed on various networks and datasets show comparable performance of SWAP with state-of-the-art (SoTA) network pruning algorithms. Our proposed method outperforms the SoTA when the network size or the target sparsity is large, the gain is even larger with the existence of noisy gradients, possibly from noisy data, analog memory, or adversarial attacks. Notably, our proposed method achieves a gain of 6% improvement in accuracy and 8% improvement in testing loss for MobileNetV1 with less than one-fourth of the network parameters remaining. | https://openreview.net/pdf/3c2e0dd487d35b88c63a497690c722e7ae3a41e4.pdf |
Circumventing Concept Erasure Methods For Text-To-Image Generative Models | https://openreview.net/forum?id=ag3o2T51Ht | https://openreview.net/forum?id=ag3o2T51Ht | Minh Pham,Kelly O. Marshall,Niv Cohen,Govind Mittal,Chinmay Hegde | ICLR 2024,Poster | Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine seven recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety. | https://openreview.net/pdf/2dac63ac9ce9ca94acff7e8db532e2c08ee9834e.pdf |
Pose Modulated Avatars from Video | https://openreview.net/forum?id=5t44vPlv9x | https://openreview.net/forum?id=5t44vPlv9x | Chunjin Song,Bastian Wandt,Helge Rhodin | ICLR 2024,Poster | It is now possible to reconstruct dynamic human motion and shape from a sparse set of cameras using Neural Radiance Fields (NeRF) driven by an underlying skeleton. However, a challenge remains to model the deformation of cloth and skin in relation to skeleton pose. Unlike existing avatar models that are learned implicitly or rely on a proxy surface, our approach is motivated by the observation that different poses necessitate unique frequency assignments. Neglecting this distinction yields noisy artifacts in smooth areas or blurs fine-grained texture and shape details in sharp regions. We develop a two-branch neural network that is adaptive and explicit in the frequency domain. The first branch is a graph neural network that models correlations among body parts locally, taking skeleton pose as input. The second branch combines these correlation features to a set of global frequencies and then modulates the feature encoding. Our experiments demonstrate that our network outperforms state-of-the-art methods in terms of preserving details and generalization capabilities. Our code is available at https://github.com/ChunjinSong/PM-Avatars. | https://openreview.net/pdf/464c5df9cfcbf6c9712ab3743fc39bfc34f76a4d.pdf |
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images | https://openreview.net/forum?id=ixP76Y33y1 | https://openreview.net/forum?id=ixP76Y33y1 | Nicholas Konz,Maciej A Mazurowski | ICLR 2024,Poster | This paper investigates discrepancies in how neural networks learn from different imaging domains, which are commonly overlooked when adopting computer vision techniques from the domain of natural images to other specialized domains such as medical images. Recent works have found that the generalization error of a trained network typically increases with the intrinsic dimension ($d_{data}$) of its training set. Yet, the steepness of this relationship varies significantly between medical (radiological) and natural imaging domains, with no existing theoretical explanation. We address this gap in knowledge by establishing and empirically validating a generalization scaling law with respect to $d_{data}$, and propose that the substantial scaling discrepancy between the two considered domains may be at least partially attributed to the higher intrinsic ``label sharpness'' ($K_\mathcal{F}$) of medical imaging datasets, a metric which we propose. Next, we demonstrate an additional benefit of measuring the label sharpness of a training set: it is negatively correlated with the trained model's adversarial robustness, which notably leads to models for medical images having a substantially higher vulnerability to adversarial attack. Finally, we extend our $d_{data}$ formalism to the related metric of learned representation intrinsic dimension ($d_{repr}$), derive a generalization scaling law with respect to $d_{repr}$, and show that $d_{data}$ serves as an upper bound for $d_{repr}$. Our theoretical results are supported by thorough experiments with six models and eleven natural and medical imaging datasets over a range of training set sizes. Our findings offer insights into the influence of intrinsic dataset properties on generalization, representation learning, and robustness in deep neural networks. *Code link: https://github.com/mazurowski-lab/intrinsic-properties* | https://openreview.net/pdf/cde1ddb8421d878f07481ed35ca6a7eda622e2b3.pdf |
Complete and Efficient Graph Transformers for Crystal Material Property Prediction | https://openreview.net/forum?id=BnQY9XiRAS | https://openreview.net/forum?id=BnQY9XiRAS | Keqiang Yan,Cong Fu,Xiaofeng Qian,Xiaoning Qian,Shuiwang Ji | ICLR 2024,Poster | Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). | https://openreview.net/pdf/d25b06fbca76b8ea41b195b65471fab55cc08945.pdf |
Patched Denoising Diffusion Models For High-Resolution Image Synthesis | https://openreview.net/forum?id=TgSRPRz8cI | https://openreview.net/forum?id=TgSRPRz8cI | Zheng Ding,Mengqi Zhang,Jiajun Wu,Zhuowen Tu | ICLR 2024,Poster | We propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024$\times$512), trained on small-size image patches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a new feature collage strategy is designed to avoid the boundary artifact when synthesizing large-size images. Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space. Patch-DM produces high-quality image synthesis results on our newly collected dataset of nature images (1024$\times$512), as well as on standard benchmarks of LHQ(1024$\times$ 1024), FFHQ(1024$\times$ 1024) and on other datasets with smaller sizes (256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare our method with previous patch-based generation methods and achieve state-of-the-art FID scores on all six datasets. Further, Patch-DM also reduces memory complexity compared to the classic diffusion models. Project page: https://patchdm.github.io. | https://openreview.net/pdf/62fb6f5b691fdfac6ae76d17dab711eae7cfc3af.pdf |
NOLA: Compressing LoRA using Linear Combination of Random Basis | https://openreview.net/forum?id=TjfXcDgvzk | https://openreview.net/forum?id=TjfXcDgvzk | Soroush Abbasi Koohpayegani,Navaneet K L,Parsa Nooralinejad,Soheil Kolouri,Hamed Pirsiavash | ICLR 2024,Poster | Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3).
Current literature, such as LoRA, showcases the potential of low-rank modifications to the original weights of an LLM, enabling efficient adaptation and storage for task-specific models. These methods can reduce the number of parameters needed to fine-tune an LLM by several orders of magnitude. Yet, these methods face two primary limitations: (1) the parameter count is lower-bounded by the rank one decomposition, and (2) the extent of reduction is heavily influenced by both the model architecture and the chosen rank. We introduce NOLA, which overcomes the rank one lower bound present in LoRA. It achieves this by re-parameterizing the low-rank matrices in LoRA using linear combinations of randomly generated matrices (basis) and optimizing the linear mixture coefficients only. This approach allows us to decouple the number of trainable parameters from both the choice of rank and the network architecture. We present adaptation results using GPT-2, LLaMA-2, and ViT in natural language and computer vision tasks. NOLA performs as well as LoRA models with much fewer number of parameters compared to LoRA with rank one, the best compression LoRA can archive. Particularly, on LLaMA-2 70B, our method is almost 20 times more compact than the most compressed LoRA without degradation in accuracy. Our code is available here: https://github.com/UCDvision/NOLA | https://openreview.net/pdf/076720303e74d73bcea09773dad44a6285bd020b.pdf |
Unveiling Options with Neural Network Decomposition | https://openreview.net/forum?id=a8VETFwcVR | https://openreview.net/forum?id=a8VETFwcVR | Mahdi Alikhasi,Levi Lelis | ICLR 2024,Poster | In reinforcement learning, agents often learn policies for specific tasks without the ability to generalize this knowledge to related tasks. This paper introduces an algorithm that attempts to address this limitation by decomposing neural networks encoding policies for Markov Decision Processes into reusable sub-policies, which are used to synthesize temporally extended actions, or options. We consider neural networks with piecewise linear activation functions, so that they can be mapped to an equivalent tree that is similar to oblique decision trees. Since each node in such a tree serves as a function of the input of the tree, each sub-tree is a sub-policy of the main policy. We turn each of these sub-policies into options by wrapping it with while-loops of varied number of iterations. Given the large number of options, we propose a selection mechanism based on minimizing the Levin loss for a uniform policy on these options. Empirical results in two grid-world domains where exploration can be difficult confirm that our method can identify useful options, thereby accelerating the learning process on similar but different tasks. | https://openreview.net/pdf/1512d80930de6f07314daf2005bc48f33966759b.pdf |
HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance | https://openreview.net/forum?id=IZMPWmcS3H | https://openreview.net/forum?id=IZMPWmcS3H | Junzhe Zhu,Peiye Zhuang,Sanmi Koyejo | ICLR 2024,Poster | The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like Neural Radiance Fields (NeRFs) via latent-space denoising score matching. Yet, these methods often result in artifacts and inconsistencies across different views due to their suboptimal optimization approaches and limited understanding of 3D geometry. Moreover, the inherent constraints of NeRFs in rendering crisp geometry and stable textures usually lead to a two-stage optimization to attain high-resolution details. This work proposes holistic sampling and smoothing approaches to achieve high-quality text-to-3D generation, all in a single-stage optimization. We compute denoising scores in the text-to-image diffusion model's latent and image spaces. Instead of randomly sampling timesteps (also referred to as noise levels in denoising score matching), we introduce a novel timestep annealing approach that progressively reduces the sampled timestep throughout optimization. To generate high-quality renderings in a single-stage optimization, we propose regularization for the variance of z-coordinates along NeRF rays. To address texture flickering issues in NeRFs, we introduce a kernel smoothing technique that refines importance sampling weights coarse-to-fine, ensuring accurate and thorough sampling in high-density regions. Extensive experiments demonstrate the superiority of our method over previous approaches, enabling the generation of highly detailed and view-consistent 3D assets through a single-stage training process. | https://openreview.net/pdf/a8f1bf6be55ca7fc6fcd410b7de567ff35981853.pdf |
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing | https://openreview.net/forum?id=JgqftqZQZ7 | https://openreview.net/forum?id=JgqftqZQZ7 | Yuren Cong,Mengmeng Xu,christian simon,Shoufa Chen,Jiawei Ren,Yanping Xie,Juan-Manuel Perez-Rua,Bodo Rosenhahn,Tao Xiang,Sen He | ICLR 2024,Poster | Text-to-video editing aims to edit the visual appearance of a source video conditional on textual prompts.
A major challenge in this task is to ensure that all frames in the edited video are visually consistent.
Most recent works apply advanced text-to-image diffusion models to this task by inflating 2D spatial attention in the U-Net into spatio-temporal attention.
Although temporal context can be added through spatio-temporal attention, it may introduce some irrelevant information for each patch and therefore cause inconsistency in the edited video.
In this paper, for the first time, we introduce optical flow into the attention module in diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Our method, FLATTEN, enforces the patches on the same flow path across different frames to attend to each other in the attention module, thus improving the visual consistency in the edited videos.
Additionally, our method is training-free and can be seamlessly integrated into any diffusion based text-to-video editing methods and improve their visual consistency.
Experiment results on existing text-to-video editing benchmarks show that our proposed method achieves the new state-of-the-art performance. In particular, our method excels in maintaining the visual consistency in the edited videos. | https://openreview.net/pdf/c2e1ab78aec0d5b7b0d3cd5cb8fd8ad2f4519fdc.pdf |
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? | https://openreview.net/forum?id=jlEjB8MVGa | https://openreview.net/forum?id=jlEjB8MVGa | Xuefeng Du,Zhen Fang,Ilias Diakonikolas,Yixuan Li | ICLR 2024,Poster | Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data. This lack of a clean set of OOD samples poses significant challenges in learning an optimal OOD classifier. Currently, there is a lack of research on formally understanding how unlabeled data helps OOD detection. This paper bridges the gap by introducing a new learning framework SAL (Separate And Learn) that offers both strong theoretical guarantees and empirical effectiveness. The framework separates candidate outliers from the unlabeled data and then trains an OOD classifier using the candidate outliers and the labeled ID data. Theoretically, we provide rigorous error bounds from the lens of separability and learnability, formally justifying the two components in our algorithm. Our theory shows that SAL can separate the candidate outliers with small error rates, which leads to a generalization guarantee for the learned OOD classifier. Empirically, SAL achieves state-of-the-art performance on common benchmarks, reinforcing our theoretical insights. Code is publicly available at https://github.com/deeplearning-wisc/sal. | https://openreview.net/pdf/8ddddb98f62ba8bc52eaeeb06e89d32791f87063.pdf |
Delta-AI: Local objectives for amortized inference in sparse graphical models | https://openreview.net/forum?id=LemSSn8htt | https://openreview.net/forum?id=LemSSn8htt | Jean-Pierre René Falet,Hae Beom Lee,Nikolay Malkin,Chen Sun,Dragos Secrieru,Dinghuai Zhang,Guillaume Lajoie,Yoshua Bengio | ICLR 2024,Poster | We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI). Our approach is based on the observation that when the sampling of variables in a PGM is seen as a sequence of actions taken by an agent, sparsity of the PGM enables local credit assignment in the agent's policy learning objective. This yields a local constraint that can be turned into a local loss in the style of generative flow networks (GFlowNets) that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably. The $\Delta$-AI objective matches the conditional distribution of a variable given its Markov blanket in a tractable learned sampler, which has the structure of a Bayesian network, with the same conditional distribution under the target PGM. As such, the trained sampler recovers marginals and conditional distributions of interest and enables inference of partial subsets of variables. We illustrate $\Delta$-AI's effectiveness for sampling from synthetic PGMs and training latent variable models with sparse factor structure. Code: https://github.com/GFNOrg/Delta-AI. | https://openreview.net/pdf/05a032243d28184a3102f9fc03c775e53cc11f84.pdf |
Learning Implicit Representation for Reconstructing Articulated Objects | https://openreview.net/forum?id=KQ2i6jazVK | https://openreview.net/forum?id=KQ2i6jazVK | Hao Zhang,Fang Li,Samyak Rawlekar,Narendra Ahuja | ICLR 2024,Poster | 3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employing category-specific skeletal models. Consequently, they do not generalize well to articulated objects in the wild. We treat an articulated object as an unknown, semi-rigid skeletal structure surrounded by nonrigid material (e.g., skin). Our method simultaneously estimates the visible (explicit) representation (3D shapes, colors, camera parameters) and the underlying (implicit) skeletal representation, from motion cues in the object video without 3D supervision. Our implicit representation consists of four parts. (1) skeleton, which specifies which semi-rigid parts are connected. (2) Semi-rigid Part Assignment, which associates each surface vertex with a semi-rigid part. (3) Rigidity Coefficients, specifying the articulation of the local surface. (4) Time-Varying Transformations, which specify the skeletal motion and surface deformation parameters. We introduce an algorithm that uses these constraints as regularization terms and iteratively estimates both implicit and explicit representations. Our method is category-agnostic, thus eliminating the need for category-specific skeletons, we show that our method outperforms state-of-the-art across standard video datasets. | https://openreview.net/pdf/6cb59db44f8c5732435a1586fca4dd9de9300a45.pdf |
Improving protein optimization with smoothed fitness landscapes | https://openreview.net/forum?id=rxlF2Zv8x0 | https://openreview.net/forum?id=rxlF2Zv8x0 | Andrew Kirjner,Jason Yim,Raman Samusevich,Shahar Bracha,Tommi S. Jaakkola,Regina Barzilay,Ila R Fiete | ICLR 2024,Poster | The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of sequences is infeasible; prior methods often constrain optimization to a small mutational radius, but this drastically limits the design space. Instead of heuristics, we propose smoothing the fitness landscape to facilitate protein optimization. First, we formulate protein fitness as a graph signal then use Tikunov regularization to smooth the fitness landscape. We find optimizing in this smoothed landscape leads to improved performance across multiple methods in the GFP and AAV benchmarks. Second, we achieve state-of-the-art results utilizing discrete energy-based models and MCMC in the smoothed landscape. Our method, called Gibbs sampling with Graph-based Smoothing (GGS), demonstrates a unique ability to achieve 2.5 fold fitness improvement (with in-silico evaluation) over its training set. GGS demonstrates potential to optimize proteins in the limited data regime. Code: https://github.com/kirjner/GGS | https://openreview.net/pdf/4beb63a2e06d9568e786f8d9f4ffc8e36d04c6c3.pdf |
Rethinking Label Poisoning for GNNs: Pitfalls and Attacks | https://openreview.net/forum?id=J7ioefqDPw | https://openreview.net/forum?id=J7ioefqDPw | Vijay Lingam,Mohammad Sadegh Akhondzadeh,Aleksandar Bojchevski | ICLR 2024,Poster | Node labels for graphs are usually generated using an automated process or crowd-sourced from human users. This opens up avenues for malicious users to compromise the training labels, making it unwise to blindly rely on them. While robustness against noisy labels is an active area of research, there are only a handful of papers in the literature that address this for graph-based data. Even more so, the effects of adversarial label perturbations is sparsely studied. More critically, we reveal that the entire literature on label poisoning for GNNs is plagued by serious evaluation pitfalls. Thus making it hard to conclude how robust GNNs are against label perturbations. After course correcting the state of label poisoning attacks with our faithful evaluation, we identify a discrepancy in attack efficiency of $\sim9\%$ on average. Additionally, we introduce two new simple yet effective attacks that are significantly stronger (up to $\sim8\%$) than the previous strongest attack. Our strongest proposed attack can be efficiently computed and is theoretically backed. | https://openreview.net/pdf/27420ebe98b37fb5b837c1aaa6965dd5139c68a5.pdf |
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability | https://openreview.net/forum?id=nHkMm0ywWm | https://openreview.net/forum?id=nHkMm0ywWm | Songyao Jin,Feng Xie,Guangyi Chen,Biwei Huang,Zhengming Chen,Xinshuai Dong,Kun Zhang | ICLR 2024,Poster | Conventional causal discovery approaches, which seek to uncover causal relationships among measured variables, are typically fragile to the presence of latent variables. While various methods have been developed to address this confounding issue, they often rely on strong assumptions about the underlying causal structure. In this paper, we consider a general scenario where measured and latent variables collectively form a partially observed causally sufficient linear system and latent variables may be anywhere in the causal structure. We theoretically show that with the aid of high-order statistics, the causal graph is (almost) fully identifiable if, roughly speaking, each latent set has a sufficient number of pure children, which can be either latent or measured. Naturally, LiNGAM, a model without latent variables, is encompassed as a special case. Based on the identification theorem, we develop a principled algorithm to identify the causal graph by testing for statistical independence involving only measured variables in specific manners. Experimental results show that our method effectively recovers the causal structure, even when latent variables are influenced by measured variables. | https://openreview.net/pdf/48234ceb5e90b4323842e3329f4f30810a293340.pdf |
Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis | https://openreview.net/forum?id=bJ3gFiwRgi | https://openreview.net/forum?id=bJ3gFiwRgi | Shicheng Liu,Minghui Zhu | ICLR 2024,Poster | This paper considers the problem of learning the reward function and constraints of an expert from few demonstrations. This problem can be considered as a meta-learning problem where we first learn meta-priors over reward functions and constraints from other distinct but related tasks and then adapt the learned meta-priors to new tasks from only few expert demonstrations. We formulate a bi-level optimization problem where the upper level aims to learn a meta-prior over reward functions and the lower level is to learn a meta-prior over constraints. We propose a novel algorithm to solve this problem and formally guarantee that the algorithm reaches the set of $\epsilon$-stationary points at the iteration complexity $O(\frac{1}{\epsilon^2})$. We also quantify the generalization error to an arbitrary new task. Experiments are used to validate that the learned meta-priors can adapt to new tasks with good performance from only few demonstrations. | https://openreview.net/pdf/9021eb40a21a6c7a3ca8e7125ad93a6e174f2420.pdf |
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform | https://openreview.net/forum?id=Diq6urt3lS | https://openreview.net/forum?id=Diq6urt3lS | Shengyi Huang,Jiayi Weng,Rujikorn Charakorn,Min Lin,Zhongwen Xu,Santiago Ontanon | ICLR 2024,Poster | Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time. Despite recent progress in the field, reproducibility issues have not been sufficiently explored. This paper first shows that the typical actor-learner framework can have reproducibility issues even if hyperparameters are controlled. We then introduce Cleanba, a new open-source platform for distributed DRL that proposes a highly reproducible architecture. Cleanba implements highly optimized distributed variants of PPO and IMPALA. Our Atari experiments show that these variants can obtain equivalent or higher scores than strong IMPALA baselines in moolib and torchbeast and PPO baseline in CleanRL. However, Cleanba variants present 1) shorter training time and 2) more reproducible learning curves in different hardware settings. | https://openreview.net/pdf/caf45411ab9213f04b1a43aecc40e0c555b2fd5a.pdf |
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning | https://openreview.net/forum?id=eo9dHwtTFt | https://openreview.net/forum?id=eo9dHwtTFt | Harry Zhao,Safa Alver,Harm van Seijen,Romain Laroche,Doina Precup,Yoshua Bengio | ICLR 2024,Poster | Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper’s significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods. | https://openreview.net/pdf/ede954be5afe182bc87c085f981cac2f0ef1509f.pdf |
Bayesian Low-rank Adaptation for Large Language Models | https://openreview.net/forum?id=FJiUyzOF1m | https://openreview.net/forum?id=FJiUyzOF1m | Adam X. Yang,Maxime Robeyns,Xi Wang,Laurence Aitchison | ICLR 2024,Poster | Parameter-efficient fine-tuning (PEFT) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs), with low-rank adaptation (LoRA) being a widely adopted choice. However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets. Bayesian methods, with their inherent ability to estimate uncertainty, serve as potent tools to mitigate overconfidence and enhance calibration. In this work, we introduce Laplace-LoRA, a straightforward yet effective Bayesian method, which applies the Laplace approximation to the LoRA parameters and, considerably boosts the calibration of fine-tuned LLMs. | https://openreview.net/pdf/4dea14bb3bd06271b678fe6d24bf3d1e605e81cb.pdf |
Function-space Parameterization of Neural Networks for Sequential Learning | https://openreview.net/forum?id=2dhxxIKhqz | https://openreview.net/forum?id=2dhxxIKhqz | Aidan Scannell,Riccardo Mereu,Paul Edmund Chang,Ella Tamir,Joni Pajarinen,Arno Solin | ICLR 2024,Poster | Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images. To address these issues, we introduce a technique that converts neural networks from weight space to function space, through a dual parameterization. Our parameterization offers: (*i*) a way to scale function-space methods to large data sets via sparsification, (*ii*) retention of prior knowledge when access to past data is limited, and (*iii*) a mechanism to incorporate new data without retraining. Our experiments demonstrate that we can retain knowledge in continual learning and incorporate new data efficiently. We further show its strengths in uncertainty quantification and guiding exploration in model-based RL. Further information and code is available on the project website. | https://openreview.net/pdf/7d87f95bf472ce1e0650d2de9c7d241f30b0344d.pdf |
DENEVIL: TOWARDS DECIPHERING AND NAVIGATING THE ETHICAL VALUES OF LARGE LANGUAGE MODELS VIA INSTRUCTION LEARNING | https://openreview.net/forum?id=m3RRWWFaVe | https://openreview.net/forum?id=m3RRWWFaVe | Shitong Duan,Xiaoyuan Yi,Peng Zhang,Tun Lu,Xing Xie,Ning Gu | ICLR 2024,Poster | Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical values utilizing Moral Foundation Theory. Moving beyond conventional discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs’ value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that substantially enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, offering a promising initial step in studying the ethical values of LLMs. | https://openreview.net/pdf/ca62b3d6c8d582899825304c6ee96c7b6281fb7a.pdf |
The Expressive Power of Transformers with Chain of Thought | https://openreview.net/forum?id=NjNGlPh8Wh | https://openreview.net/forum?id=NjNGlPh8Wh | William Merrill,Ashish Sabharwal | ICLR 2024,Poster | Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after reading their input. However, in practice, transformers' reasoning can be improved by allowing them to use a "chain of thought" or "scratchpad", i.e., generate and condition on a sequence of intermediate tokens before answering. Motivated by this, we ask: *Does such intermediate generation fundamentally extend the computational power of a decoder-only transformer?* We show that the answer is *yes*, but the amount of increase depends crucially on the amount of intermediate generation. For instance, we find that transformer decoders with a logarithmic number of decoding steps (w.r.t. the input length) push the limits of standard transformers only slightly, while a linear number of decoding steps, assuming projected pre-norm (a slight generalization of standard pre-norm), adds a clear new ability (under standard complexity conjectures): recognizing all regular languages. Our results also imply that linear steps keep transformer decoders within context-sensitive languages, and polynomial steps with generalized pre-norm make them recognize exactly the class of polynomial-time solvable problems—the first exact characterization of a type of transformers in terms of standard complexity classes. Together, this provides a nuanced framework for understanding how the length of a transformer’s chain of thought or scratchpad impacts its reasoning power. | https://openreview.net/pdf/c681a40313eba63d16deacf4825e40d9935cd222.pdf |
When should we prefer Decision Transformers for Offline Reinforcement Learning? | https://openreview.net/forum?id=vpV7fOFQy4 | https://openreview.net/forum?id=vpV7fOFQy4 | Prajjwal Bhargava,Rohan Chitnis,Alborz Geramifard,Shagun Sodhani,Amy Zhang | ICLR 2024,Poster | Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset. Three popular algorithms for offline RL are Conservative Q-Learning (CQL), Behavior Cloning (BC), and Decision Transformer (DT), from the class of Q-Learning, Imitation Learning, and Sequence Modeling respectively. A key open question is: which algorithm is preferred under what conditions? We study this question empirically by exploring the performance of these algorithms across the commonly used D4RL and Robomimic benchmarks. We design targeted experiments to understand their behavior concerning data suboptimality, task complexity, and stochasticity. Our key findings are: (1) DT requires more data than CQL to learn competitive policies but is more robust; (2) DT is a substantially better choice than both CQL and BC in sparse-reward and low-quality data settings; (3) DT and BC are preferable as task horizon increases, or when data is obtained from human demonstrators; and (4) CQL excels in situations characterized by the combination of high stochasticity and low data quality. We also investigate architectural choices and scaling trends for DT on \textsc{atari} and D4RL and make design/scaling recommendations. We find that scaling the amount of data for DT by 5x gives a 2.5x average score improvement on Atari. | https://openreview.net/pdf/9c6b2993fd7eb98d8a91155e81691f99c2e9aedd.pdf |
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate | https://openreview.net/forum?id=FQepisCUWu | https://openreview.net/forum?id=FQepisCUWu | Chi-Min Chan,Weize Chen,Yusheng Su,Jianxuan Yu,Wei Xue,Shanghang Zhang,Jie Fu,Zhiyuan Liu | ICLR 2024,Poster | Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results suggest that further advancements are needed to bridge the gap between their current effectiveness and human-level evaluation quality.
Recognizing that best practices of human evaluation processes often involve multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies.
In this paper, we construct a multi-agent referee team called $\textbf{ChatEval}$ to autonomously discuss and evaluate the quality of different texts.
Our experiments on two benchmarks illustrate that ChatEval delivers superior accuracy and correlation in alignment with human assessment. Furthermore, we find that the diverse role prompts (different personas) are essential in the multi-agent debate process; that is, utilizing the same role description in the prompts can lead to a degradation in performance. Our qualitative analysis also shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments. | https://openreview.net/pdf/6950b70dde4f8d7425424dce9cc4063f505b2194.pdf |
Bridging State and History Representations: Understanding Self-Predictive RL | https://openreview.net/forum?id=ms0VgzSGF2 | https://openreview.net/forum?id=ms0VgzSGF2 | Tianwei Ni,Benjamin Eysenbach,Erfan SeyedSalehi,Michel Ma,Clement Gehring,Aditya Mahajan,Pierre-Luc Bacon | ICLR 2024,Poster | Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners. | https://openreview.net/pdf/fd7e94a3c07003dcb25491eca4944e8d4c6ca352.pdf |
TapMo: Shape-aware Motion Generation of Skeleton-free Characters | https://openreview.net/forum?id=OeH6Fdhv7q | https://openreview.net/forum?id=OeH6Fdhv7q | Jiaxu Zhang,Shaoli Huang,Zhigang Tu,Xin Chen,Xiaohang Zhan,Gang YU,Ying Shan | ICLR 2024,Poster | Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters. In this work, we present TapMo, a Text-driven Animation PIpeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters. The pivotal innovation in TapMo is its use of shape deformation-aware features as a condition to guide the diffusion model, thereby enabling the generation of mesh-specific motions for various characters. Specifically, TapMo comprises two main components - Mesh Handle Predictor and Shape-aware Diffusion Module. Mesh Handle Predictor predicts the skinning weights and clusters mesh vertices into adaptive handles for deformation control, which eliminates the need for traditional skeletal rigging. Shape-aware Motion Diffusion synthesizes motion with mesh-specific adaptations. This module employs text-guided motions and mesh features extracted during the first stage, preserving the geometric integrity of the animations by accounting for the character's shape and deformation. Trained in a weakly-supervised manner, TapMo can accommodate a multitude of non-human meshes, both with and without associated text motions. We demonstrate the effectiveness and generalizability of TapMo through rigorous qualitative and quantitative experiments. Our results reveal that TapMo consistently outperforms existing auto-animation methods, delivering superior-quality animations for both seen or unseen heterogeneous 3D characters. | https://openreview.net/pdf/eaf38c128dca2c1b7c1287fadcae96f534386467.pdf |
InstructDET: Diversifying Referring Object Detection with Generalized Instructions | https://openreview.net/forum?id=hss35aoQ1Y | https://openreview.net/forum?id=hss35aoQ1Y | Ronghao Dang,Jiangyan Feng,Haodong Zhang,Chongjian GE,Lin Song,Lijun GONG,Chengju Liu,Qijun Chen,Feng Zhu,Rui Zhao,Yibing Song | ICLR 2024,Poster | We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressions (REC), the instructions we leverage are greatly diversified to encompass common user intentions related to object detection. For one image, we produce tremendous instructions that refer to every single object and different combinations of multiple objects. Each instruction and its corresponding object bounding boxes (bbxs) constitute one training data pair. In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e.g., describing object property, category, and relationship). We name our constructed dataset as InDET. It contains images, bbxs and generalized instructions that are from foundation models. Our InDET is developed from existing REC datasets and object detection datasets, with the expanding potential that any image with object bbxs can be incorporated through using our InstructDET method. By using our InDET dataset, we show that a conventional ROD model surpasses existing methods on standard REC datasets and our InDET test set. Our data-centric method InstructDET, with automatic data expansion by leveraging foundation models, directs a promising field that ROD can be greatly diversified to execute common object detection instructions. | https://openreview.net/pdf/6d9b5bce3da62b2c3893005b8c52b3a547442d1e.pdf |
RAIN: Your Language Models Can Align Themselves without Finetuning | https://openreview.net/forum?id=pETSfWMUzy | https://openreview.net/forum?id=pETSfWMUzy | Yuhui Li,Fangyun Wei,Jinjing Zhao,Chao Zhang,Hongyang Zhang | ICLR 2024,Poster | Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, a.k.a. the finetuning step. In contrast, aligning frozen LLMs without requiring alignment data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide rewind and generation for AI safety. Notably, RAIN operates without the need of extra data for model alignment and abstains from any training, gradient computation, or parameter updates. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate of LLaMA 30B from 82% of vanilla inference to 97%, while maintaining the helpfulness rate. On the TruthfulQA dataset, RAIN improves the truthfulness of the already-well-aligned LLaMA-2-chat 13B model by 5%. | https://openreview.net/pdf/9e3e1e6453dad8871eb7f75b7404154c09c56782.pdf |
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