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Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression | https://openreview.net/forum?id=D5mJSNtUtv | https://openreview.net/forum?id=D5mJSNtUtv | Yufeng Zhang,Hang Yu,Jianguo Li,Weiyao Lin | ICLR 2024,Spotlight | Learned lossless data compression has garnered significant attention recently due to its superior compression ratios compared to traditional compressors. However, the computational efficiency of these models jeopardizes their practicality. This paper proposes a novel system for improving the compression ratio while maintaining computational efficiency for learned lossless data compression. Our approach incorporates two essential innovations. First, we propose the Finite-State AutoRegressive (FSAR) entropy coder, an efficient autoregressive Markov model based entropy coder that utilizes a lookup table to expedite autoregressive entropy coding. Next, we present a Straight-Through Hardmax Quantization (STHQ) scheme to enhance the optimization of discrete latent space. Our experiments show that the proposed lossless compression method could improve the compression ratio by up to 6\% compared to the baseline, with negligible extra computational time. Our work provides valuable insights into enhancing the computational efficiency of learned lossless data compression, which can have practical applications in various fields. Code is available at https://github.com/alipay/Finite_State_Autoregressive_Entropy_Coding. | https://openreview.net/pdf/5594d916eebbbc22170cfdf6b5c25177191835fc.pdf |
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs | https://openreview.net/forum?id=dHng2O0Jjr | https://openreview.net/forum?id=dHng2O0Jjr | Yujia Qin,Shihao Liang,Yining Ye,Kunlun Zhu,Lan Yan,Yaxi Lu,Yankai Lin,Xin Cong,Xiangru Tang,Bill Qian,Sihan Zhao,Lauren Hong,Runchu Tian,Ruobing Xie,Jie Zhou,Mark Gerstein,dahai li,Zhiyuan Liu,Maosong Sun | ICLR 2024,Spotlight | Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excellent tool-use capabilities of state-of-the-art (SOTA) closed-source LLMs, e.g., ChatGPT. To bridge this gap, we introduce ToolLLM, a general tool-use framework encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is constructed automatically using ChatGPT. Specifically, the construction can be divided into three stages: (i) API collection: we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub; (ii) instruction generation: we prompt ChatGPT to generate diverse instructions involving these APIs, covering both single-tool and multi-tool scenarios; (iii) solution path annotation: we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To enhance the reasoning capabilities of LLMs, we develop a novel depth-first search-based decision tree algorithm. It enables LLMs to evaluate multiple reasoning traces and expand the search space. Moreover, to evaluate the tool-use capabilities of LLMs, we develop an automatic evaluator: ToolEval. Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction. Experiments show that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. Our ToolLLaMA also demonstrates strong zero-shot generalization ability in an out-of-distribution tool-use dataset: APIBench. | https://openreview.net/pdf/ccd998057a57b2ae3f5615915e57554028f1ae35.pdf |
Enhanced Face Recognition using Intra-class Incoherence Constraint | https://openreview.net/forum?id=uELjxVbrqG | https://openreview.net/forum?id=uELjxVbrqG | Yuanqing Huang,Yinggui Wang,Le Yang,Lei Wang | ICLR 2024,Spotlight | The current face recognition (FR) algorithms has achieved a high level of accuracy, making further improvements increasingly challenging. While existing FR algorithms primarily focus on optimizing margins and loss functions, limited attention has been given to exploring the feature representation space. Therefore, this paper endeavors to improve FR performance in the view of feature representation space. Firstly, we consider two FR models that exhibit distinct performance discrepancies, where one model exhibits superior recognition accuracy compared to the other. We implement orthogonal decomposition on the features from the superior model along those from the inferior model and obtain two sub-features. Surprisingly, we find the sub-feature perpendicular to the inferior still possesses a certain level of face distinguishability. We adjust the modulus of the sub-features and recombine them through vector addition. Experiments demonstrate this recombination is likely to contribute to an improved facial feature representation, even better than features from the original superior model. Motivated by this discovery, we further consider how to improve FR accuracy when there is only one FR model available. Inspired by knowledge distillation, we incorporate the intra-class incoherence constraint (IIC) to solve the problem. Experiments on various FR benchmarks show the existing state-of-the-art method with IIC can be further improved, highlighting its potential to further enhance FR performance. | https://openreview.net/pdf/64f49baaba5e3d14bb727d898b334143acfde5f7.pdf |
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors | https://openreview.net/forum?id=9w3iw8wDuE | https://openreview.net/forum?id=9w3iw8wDuE | Jonghyun Lee,Dahuin Jung,Saehyung Lee,Junsung Park,Juhyeon Shin,Uiwon Hwang,Sungroh Yoon | ICLR 2024,Spotlight | Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://whitesnowdrop.github.io/DeYO/. | https://openreview.net/pdf/efba9a12bfc53499b73044f7ba478887cccc7d77.pdf |
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution | https://openreview.net/forum?id=CGlczSBBSj | https://openreview.net/forum?id=CGlczSBBSj | Wenlong Zhang,Xiaohui Li,Xiangyu Chen,Xiaoyun Zhang,Yu Qiao,Xiao-Ming Wu,Chao Dong | ICLR 2024,Spotlight | Real-world Super-Resolution (Real-SR) methods focus on dealing with diverse real-world images and have attracted increasing attention in recent years. The key idea is to use a complex and high-order degradation model to mimic real-world degradations.
Although they have achieved impressive results in various scenarios, they are faced with the obstacle of evaluation. Currently, these methods are only assessed by their average performance on a small set of degradation cases randomly selected from a large space, which fails to provide a comprehensive understanding of their overall performance and often yields inconsistent and potentially misleading results.
To overcome the limitation in evaluation, we propose SEAL, a framework for systematic evaluation of real-SR. In particular, we cluster the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set. Next, we propose a coarse-to-fine evaluation protocol to measure the distributed and relative performance of real-SR methods on the test set. The protocol incorporates two new metrics: acceptance rate (AR) and relative performance ratio (RPR), derived from acceptance and excellence lines. Under SEAL, we benchmark existing real-SR methods, obtain new observations and insights into their performance, and develop a new strong baseline. We consider SEAL as the first step towards creating an unbiased and comprehensive real-SR evaluation platform, which can promote the development of real-SR. | https://openreview.net/pdf/ef2c19059bfaeab85c449243ad90b25a62910da9.pdf |
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood | https://openreview.net/forum?id=AyzkDpuqcl | https://openreview.net/forum?id=AyzkDpuqcl | Yaxuan Zhu,Jianwen Xie,Ying Nian Wu,Ruiqi Gao | ICLR 2024,Spotlight | Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming, and there exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. To close this gap, inspired by the recent efforts of learning EBMs by maximimizing diffusion recovery likelihood (DRL), we propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs defined on increasingly noisy versons of a dataset, paired with an initializer model for each EBM. At each noise level, the two models are jointly estimated within a cooperative training framework: Samples from the initializer serve as starting points that are refined by a few MCMC sampling steps from the EBM. The EBM is then optimized by maximizing recovery likelihood, while the initializer model is optimized by learning from the difference between the refined samples and the initial samples. In addition, we made several practical designs for EBM training to further improve the sample quality. Combining these advances, we significantly boost the generation performance compared to existing EBM methods on CIFAR-10 and ImageNet 32x32. And we have shown that CDRL has great potential to largely reduce the sampling time. We also demonstrate the effectiveness of our models for several downstream tasks, including classifier-free guided generation, compositional generation, image inpainting and out-of-distribution detection. | https://openreview.net/pdf/2e17cdd8ba006f1c205e1971f050d6cb5e8d572d.pdf |
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning | https://openreview.net/forum?id=yLClGs770I | https://openreview.net/forum?id=yLClGs770I | Xiang Yue,Xingwei Qu,Ge Zhang,Yao Fu,Wenhao Huang,Huan Sun,Yu Su,Wenhu Chen | ICLR 2024,Spotlight | We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on MathInstruct, our meticulously curated instruction tuning dataset. MathInstruct is compiled from 13 math datasets with intermediate rationales, six of which have rationales newly curated by us. It presents a unique hybrid of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and also ensures extensive coverage of diverse fields in math. The hybrid of CoT and PoT not only unleashes the potential of tool use but also allows different thought processes for different math problems. As a result, the MAmmoTH series substantially outperform existing open-source models on nine mathematical reasoning datasets across all scales with an average accuracy gain between 16% and 32%. Remarkably, our MAmmoTH-7B model reaches 33% on MATH (a competition-level dataset), which exceeds the best open-source 7B model (WizardMath) by 23%, and the MAmmoTH-34B model achieves 44% accuracy on MATH, even surpassing GPT-4’s CoT result. Our work underscores the importance of diverse problem coverage and the use of hybrid rationales in developing superior math generalist models. | https://openreview.net/pdf/25429cff694fecfd301ec230f0fd6fb30e9c7373.pdf |
Time Travel in LLMs: Tracing Data Contamination in Large Language Models | https://openreview.net/forum?id=2Rwq6c3tvr | https://openreview.net/forum?id=2Rwq6c3tvr | Shahriar Golchin,Mihai Surdeanu | ICLR 2024,Spotlight | Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on other tasks. We propose a straightforward yet effective method for identifying data contamination within LLMs. At its core, our approach starts by identifying potential contamination at the instance level; using this information, our approach then assesses wider contamination at the partition level. To estimate contamination of individual instances, we employ "guided instruction:" a prompt consisting of the dataset name, partition type, and the random-length initial segment of a reference instance, asking the LLM to complete it. An instance is flagged as contaminated if the LLM's output either exactly or nearly matches the latter segment of the reference. To understand if an entire partition is contaminated, we propose two ideas. The first idea marks a dataset partition as contaminated if the average overlap score with the reference instances (as measured by ROUGE-L or BLEURT) is statistically significantly better with the completions from guided instruction compared to a "general instruction" that does not include the dataset and partition name. The second idea marks a dataset partition as contaminated if a classifier based on GPT-4 with few-shot in-context learning prompt marks multiple generated completions as exact/near-exact matches of the corresponding reference instances. Our best method achieves an accuracy between 92% and 100% in detecting if an LLM is contaminated with seven datasets, containing train and test/validation partitions, when contrasted with manual evaluation by human experts. Further, our findings indicate that GPT-4 is contaminated with AG News, WNLI, and XSum datasets. | https://openreview.net/pdf/468dbd68de8bbc08d54772aa59abfabac15acc8c.pdf |
Variational Inference for SDEs Driven by Fractional Noise | https://openreview.net/forum?id=rtx8B94JMS | https://openreview.net/forum?id=rtx8B94JMS | Rembert Daems,Manfred Opper,Guillaume Crevecoeur,Tolga Birdal | ICLR 2024,Spotlight | We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness. Combining SDEs with the powerful inference capabilities of variational methods, enables the learning of representative distributions through stochastic gradient descent. However, conventional SDEs typically assume the underlying noise to follow a Brownian motion (BM), which hinders their ability to capture long-term dependencies. In contrast, fractional Brownian motion (fBM) extends BM to encompass non-Markovian dynamics, but existing methods for inferring fBM parameters are either computationally demanding or statistically inefficient.
In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis. Additionally, we provide a closed-form expression for optimal approximation coefficients and propose to use neural networks to learn the drift, diffusion and control terms within our variational posterior, leading to the variational training of neural-SDEs. In this framework, we also optimize the Hurst index, governing the nature of our fractional noise. Beyond validation on synthetic data, we contribute a novel architecture for variational latent video prediction,—an approach that, to the best of our knowledge, enables the first variational neural-SDE application to video perception. | https://openreview.net/pdf/d5ed1bbdd5e5bd4a9dd5fa78f017dc8eed8f7fbc.pdf |
Implicit regularization of deep residual networks towards neural ODEs | https://openreview.net/forum?id=AbXGwqb5Ht | https://openreview.net/forum?id=AbXGwqb5Ht | Pierre Marion,Yu-Han Wu,Michael Eli Sander,Gérard Biau | ICLR 2024,Spotlight | Residual neural networks are state-of-the-art deep learning models. Their continuous-depth analog, neural ordinary differential equations (ODEs), are also widely used. Despite their success, the link between the discrete and continuous models still lacks a solid mathematical foundation. In this article, we take a step in this direction by establishing an implicit regularization of deep residual networks towards neural ODEs, for nonlinear networks trained with gradient flow. We prove that if the network is initialized as a discretization of a neural ODE, then such a discretization holds throughout training. Our results are valid for a finite training time, and also as the training time tends to infinity provided that the network satisfies a Polyak-Łojasiewicz condition. Importantly, this condition holds for a family of residual networks where the residuals are two-layer perceptrons with an overparameterization in width that is only linear, and implies the convergence of gradient flow to a global minimum. Numerical experiments illustrate our results. | https://openreview.net/pdf/e6ce44da0d154a08f419bcc961a66debd0c7bdf7.pdf |
NetInfoF Framework: Measuring and Exploiting Network Usable Information | https://openreview.net/forum?id=KY8ZNcljVU | https://openreview.net/forum?id=KY8ZNcljVU | Meng-Chieh Lee,Haiyang Yu,Jian Zhang,Vassilis N. Ioannidis,Xiang song,Soji Adeshina,Da Zheng,Christos Faloutsos | ICLR 2024,Spotlight | Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well? More specifically, do the graph structure and the node features carry enough usable information for the task? Our goals are
(1) to develop a fast tool to measure how much information is in the graph structure and in the node features, and
(2) to exploit the information to solve the task, if there is enough.
We propose NetInfoF, a framework including NetInfoF_Probe and NetInfoF_Act, for the measurement and the exploitation of network usable information (NUI), respectively. Given a graph data, NetInfoF_Probe measures NUI without any model training, and NetInfoF_Act solves link prediction and node classification, while two modules share the same backbone.
In summary, NetInfoF has following notable advantages:
(a) General, handling both link prediction and node classification;
(b) Principled, with theoretical guarantee and closed-form solution;
(c) Effective, thanks to the proposed adjustment to node similarity;
(d) Scalable, scaling linearly with the input size.
In our carefully designed synthetic datasets, NetInfoF correctly identifies the ground truth of NUI and is the only method being robust to all graph scenarios. Applied on real-world datasets, NetInfoF wins in 11 out of 12 times on link prediction compared to general GNN baselines. | https://openreview.net/pdf/472fc3640b561e46ab9bb9189f262b42f5ef4114.pdf |
BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation | https://openreview.net/forum?id=wHLDHRkmEu | https://openreview.net/forum?id=wHLDHRkmEu | Yaoming Wang,Jin Li,XIAOPENG ZHANG,Bowen Shi,Chenglin Li,Wenrui Dai,Hongkai Xiong,Qi Tian | ICLR 2024,Spotlight | Pre-training followed by full fine-tuning has gradually been substituted by Parameter-Efficient Tuning (PET) in the field of computer vision. PET has gained popularity, especially in the context of large-scale models, due to its ability to reduce transfer learning costs and conserve hardware resources. However, existing PET approaches primarily focus on recognition tasks and typically support uni-modal optimization, while neglecting dense prediction tasks and vision language interactions. To address this limitation, we propose a novel PET framework called **B**i-direction**a**l Inte**r**twined Vision **L**anguage Effici**e**nt Tuning for **R**eferring **I**mage Segment**a**tion (**BarLeRIa**), which leverages bi-directional intertwined vision language adapters to fully exploit the frozen pre-trained models' potential in cross-modal dense prediction tasks. In BarLeRIa, two different tuning modules are employed for efficient attention, one for global, and the other for local, along with an intertwined vision language tuning module for efficient modal fusion.
Extensive experiments conducted on RIS benchmarks demonstrate the superiority of BarLeRIa over prior PET methods with a significant margin, i.e., achieving an average improvement of 5.6\%. Remarkably, without requiring additional training datasets, BarLeRIa even surpasses SOTA full fine-tuning approaches. The code is available at https://github.com/NastrondAd/BarLeRIa. | https://openreview.net/pdf/48b914767a0a6be82afb6de0bda746780fa23312.pdf |
Local Search GFlowNets | https://openreview.net/forum?id=6cFcw1Rxww | https://openreview.net/forum?id=6cFcw1Rxww | Minsu Kim,Taeyoung Yun,Emmanuel Bengio,Dinghuai Zhang,Yoshua Bengio,Sungsoo Ahn,Jinkyoo Park | ICLR 2024,Spotlight | Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space.
This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: \url{https://github.com/dbsxodud-11/ls_gfn}. | https://openreview.net/pdf/b6a0d0454e4fa4256c84c2e635908eef4469ccb2.pdf |
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products | https://openreview.net/forum?id=mhyQXJ6JsK | https://openreview.net/forum?id=mhyQXJ6JsK | Shengjie Luo,Tianlang Chen,Aditi S. Krishnapriyan | ICLR 2024,Spotlight | Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves the tensor products of irreducible representations (irreps). However, the computational complexity of such operations increases significantly as higher-order tensors are used. In this work, we propose a systematic approach to substantially accelerate the computation of the tensor products of irreps. We mathematically connect the commonly used Clebsch-Gordan coefficients to the Gaunt coefficients, which are integrals of products of three spherical harmonics. Through Gaunt coefficients, the tensor product of irreps becomes equivalent to the multiplication between spherical functions represented by spherical harmonics. This perspective further allows us to change the basis for the equivariant operations from spherical harmonics to a 2D Fourier basis. Consequently, the multiplication between spherical functions represented by a 2D Fourier basis can be efficiently computed via the convolution theorem and Fast Fourier Transforms. This transformation reduces the complexity of full tensor products of irreps from $\mathcal{O}(L^6)$ to $\mathcal{O}(L^3)$, where $L$ is the max degree of irreps. Leveraging this approach, we introduce the Gaunt Tensor Product, which serves as a new method to construct efficient equivariant operations across different model architectures. Our experiments on the Open Catalyst Project and 3BPA datasets demonstrate both the increased efficiency and improved performance of our approach. The code and models will be made publicly available at https://github.com/lsj2408/Gaunt-Tensor-Product. | https://openreview.net/pdf/02f7b65d108e84bedf6eff36f0c47e0923014ad5.pdf |
Idempotence and Perceptual Image Compression | https://openreview.net/forum?id=Cy5v64DqEF | https://openreview.net/forum?id=Cy5v64DqEF | Tongda Xu,Ziran Zhu,Dailan He,Yanghao Li,Lina Guo,Yuanyuan Wang,Zhe Wang,Hongwei Qin,Yan Wang,Jingjing Liu,Ya-Qin Zhang | ICLR 2024,Spotlight | Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec. Based on this newfound equivalence, we propose a new paradigm of perceptual image codec by inverting unconditional generative model with idempotence constraints. Our codec is theoretically equivalent to conditional generative codec, and it does not require training new models. Instead, it only requires a pre-trained mean-square-error codec and unconditional generative model. Empirically, we show that our proposed approach outperforms state-of-the-art methods such as HiFiC and ILLM, in terms of Fréchet Inception Distance (FID). The source code is provided in https://github.com/tongdaxu/Idempotence-and-Perceptual-Image-Compression. | https://openreview.net/pdf/64ab88c07b072dd26084986657e2d1311fae2331.pdf |
Forward $\chi^2$ Divergence Based Variational Importance Sampling | https://openreview.net/forum?id=HD5Y7M8Xdk | https://openreview.net/forum?id=HD5Y7M8Xdk | Chengrui Li,Yule Wang,Weihan Li,Anqi Wu | ICLR 2024,Spotlight | Maximizing the marginal log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method. However, VI can encounter challenges in achieving a high marginal log-likelihood when dealing with complicated posterior distributions. In response to this limitation, we introduce a novel variational importance sampling (VIS) approach that directly estimates and maximizes the marginal log-likelihood. VIS leverages the optimal proposal distribution, achieved by minimizing the forward $\chi^2$ divergence, to enhance marginal log-likelihood estimation. We apply VIS to various popular latent variable models, including mixture models, variational auto-encoders, and partially observable generalized linear models. Results demonstrate that our approach consistently outperforms state-of-the-art baselines, in terms of both log-likelihood and model parameter estimation. Code: \url{https://github.com/JerrySoybean/vis}. | https://openreview.net/pdf/e8661138b9bebb99452c4310f1eb71e825b8ab04.pdf |
Noisy Interpolation Learning with Shallow Univariate ReLU Networks | https://openreview.net/forum?id=GTUoTJXPBf | https://openreview.net/forum?id=GTUoTJXPBf | Nirmit Joshi,Gal Vardi,Nathan Srebro | ICLR 2024,Spotlight | Understanding how overparameterized neural networks generalize despite perfect interpolation of noisy training data is a fundamental question. Mallinar et. al. (2022) noted that neural networks seem to often exhibit ``tempered overfitting'', wherein the population risk does not converge to the Bayes optimal error, but neither does it approach infinity, yielding non-trivial generalization. However, this has not been studied rigorously. We provide the first rigorous analysis of the overfiting behaviour of regression with minimum norm ($\ell_2$ of weights), focusing on univariate two-layer ReLU networks. We show overfitting is tempered (with high probability) when measured with respect to the $L_1$ loss, but also show that the situation is more complex than suggested by Mallinar et. al., and overfitting is catastrophic with respect to the $L_2$ loss, or when taking an expectation over the training set. | https://openreview.net/pdf/aa43dd315f355d322a924dea13a754935635b4b7.pdf |
Initializing Models with Larger Ones | https://openreview.net/forum?id=dyrGMhicMw | https://openreview.net/forum?id=dyrGMhicMw | Zhiqiu Xu,Yanjie Chen,Kirill Vishniakov,Yida Yin,Zhiqiang Shen,Trevor Darrell,Lingjie Liu,Zhuang Liu | ICLR 2024,Spotlight | Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. | https://openreview.net/pdf/d6512714812d4eaa4cfab7e7cb04a9809c574f79.pdf |
DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model | https://openreview.net/forum?id=H4yQefeXhp | https://openreview.net/forum?id=H4yQefeXhp | Yinghao Xu,Hao Tan,Fujun Luan,Sai Bi,Peng Wang,Jiahao Li,Zifan Shi,Kalyan Sunkavalli,Gordon Wetzstein,Zexiang Xu,Kai Zhang | ICLR 2024,Spotlight | We propose DMV3D, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model incorporates a triplane NeRF representation and, functioning as a denoiser, can denoise noisy multi-view images via 3D NeRF reconstruction and rendering, achieving single-stage 3D generation in the 2D diffusion denoising process. We train DMV3D on large-scale multi-view image datasets of extremely diverse objects using only image reconstruction losses, without accessing 3D assets. We demonstrate state-of-the-art results for the single-image reconstruction problem where probabilistic modeling of unseen object parts is required for generating diverse reconstructions with sharp textures. We also show high-quality text-to-3D generation results outperforming previous 3D diffusion models. Our project website is at: https://dmv3d.github.io/. | https://openreview.net/pdf/dda54a3f50dec9a3cde3428a8330614a5c258d9a.pdf |
Influencer Backdoor Attack on Semantic Segmentation | https://openreview.net/forum?id=VmGRoNDQgJ | https://openreview.net/forum?id=VmGRoNDQgJ | Haoheng Lan,Jindong Gu,Philip Torr,Hengshuang Zhao | ICLR 2024,Spotlight | When a small number of poisoned samples are injected into the training dataset of a deep neural network, the network can be induced to exhibit malicious behavior during inferences, which poses potential threats to real-world applications. While they have been intensively studied in classification, backdoor attacks on semantic segmentation have been largely overlooked. Unlike classification, semantic segmentation aims to classify every pixel within a given image. In this work, we explore backdoor attacks on segmentation models to misclassify all pixels of a victim class by injecting a specific trigger on non-victim pixels during inferences, which is dubbed Influencer Backdoor Attack (IBA). IBA is expected to maintain the classification accuracy of non-victim pixels and mislead classifications of all victim pixels in every single inference and could be easily applied to real-world scenes. Based on the context aggregation ability of segmentation models, we proposed a simple, yet effective, Nearest-Neighbor trigger injection strategy. We also introduce an innovative Pixel Random Labeling strategy which maintains optimal performance even when the trigger is placed far from the victim pixels. Our extensive experiments reveal that current segmentation models do suffer from backdoor attacks, demonstrate IBA real-world applicability, and show that our proposed techniques can further increase attack performance. | https://openreview.net/pdf/81df12528640b4d855f26ec8e8c61fb38da33dcd.pdf |
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction | https://openreview.net/forum?id=noe76eRcPC | https://openreview.net/forum?id=noe76eRcPC | Peng Wang,Hao Tan,Sai Bi,Yinghao Xu,Fujun Luan,Kalyan Sunkavalli,Wenping Wang,Zexiang Xu,Kai Zhang | ICLR 2024,Spotlight | We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1.3 seconds on a single A100 GPU. PF-LRM is a highly scalable method utilizing self-attention blocks to exchange information between 3D object tokens and 2D image tokens; we predict a coarse point cloud for each view, and then use a differentiable Perspective-n-Point (PnP) solver to obtain camera poses. When trained on a huge amount of multi-view posed data of ~1M objects, PF-LRM shows strong cross-dataset generalization ability, and outperforms baseline methods by a large margin in terms of pose prediction accuracy and 3D reconstruction quality on various unseen evaluation datasets. We also demonstrate our model's applicability in downstream text/image-to-3D task with fast feed-forward inference. Our project website is at: https://totoro97.github.io/pf-lrm. | https://openreview.net/pdf/edf0c386092f3593a9e749732dcd13300f2d0ea8.pdf |
Procedural Fairness Through Decoupling Objectionable Data Generating Components | https://openreview.net/forum?id=cxfPefbu1s | https://openreview.net/forum?id=cxfPefbu1s | Zeyu Tang,Jialu Wang,Yang Liu,Peter Spirtes,Kun Zhang | ICLR 2024,Spotlight | We reveal and address the frequently overlooked yet important issue of _disguised procedural unfairness_, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for _pure procedural justice_ (Rawls, 1971; 2001), we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing _disguised procedural unfairness_, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected. | https://openreview.net/pdf/722f3994df5037dded9effb23cabfc87c0824ce6.pdf |
Vision-Language Foundation Models as Effective Robot Imitators | https://openreview.net/forum?id=lFYj0oibGR | https://openreview.net/forum?id=lFYj0oibGR | Xinghang Li,Minghuan Liu,Hanbo Zhang,Cunjun Yu,Jie Xu,Hongtao Wu,Chilam Cheang,Ya Jing,Weinan Zhang,Huaping Liu,Hang Li,Tao Kong | ICLR 2024,Spotlight | Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data.
To this end, we derive a simple and novel vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo. Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step vision-language comprehension, models sequential history information with an explicit policy head, and is slightly fine-tuned by imitation learning only on language-conditioned manipulation datasets. Such a decomposition provides RoboFlamingo the flexibility for open-loop control and deployment on low-performance platforms. By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control.
Our extensive experimental results also reveal several interesting conclusions regarding the behavior of different pre-trained VLMs on manipulation tasks. We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy. Our code will be made public upon acceptance. | https://openreview.net/pdf/a1af30de31af35eb9ee853f1020a7e7041aa3e9a.pdf |
OctoPack: Instruction Tuning Code Large Language Models | https://openreview.net/forum?id=mw1PWNSWZP | https://openreview.net/forum?id=mw1PWNSWZP | Niklas Muennighoff,Qian Liu,Armel Randy Zebaze,Qinkai Zheng,Binyuan Hui,Terry Yue Zhuo,Swayam Singh,Xiangru Tang,Leandro Von Werra,Shayne Longpre | ICLR 2024,Spotlight | Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack. | https://openreview.net/pdf/2b332f4f4e9406870d019ed23d22f771fefbe70f.pdf |
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision | https://openreview.net/forum?id=0V5TVt9bk0 | https://openreview.net/forum?id=0V5TVt9bk0 | Haoning Wu,Zicheng Zhang,Erli Zhang,Chaofeng Chen,Liang Liao,Annan Wang,Chunyi Li,Wenxiu Sun,Qiong Yan,Guangtao Zhai,Weisi Lin | ICLR 2024,Spotlight | The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs on **low-level visual perception and understanding**. To address this gap, we present **Q-Bench**, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment. **_a)_** To evaluate the low-level **_perception_** ability, we construct the **LLVisionQA** dataset, consisting of 2,990 diverse-sourced images, each equipped with a human-asked question focusing on its low-level attributes. We then measure the correctness of MLLMs on answering these questions. **_b)_** To examine the **_description_** ability of MLLMs on low-level information, we propose the **LLDescribe** dataset consisting of long expert-labelled *golden* low-level text descriptions on 499 images, and a GPT-involved comparison pipeline between outputs of MLLMs and the *golden* descriptions. **_c)_** Besides these two tasks, we further measure their visual quality **_assessment_** ability to align with human opinion scores. Specifically, we design a softmax-based strategy that enables MLLMs to predict *quantifiable* quality scores, and evaluate them on various existing image quality assessment (IQA) datasets. Our evaluation across the three abilities confirms that MLLMs possess preliminary low-level visual skills. However, these skills are still unstable and relatively imprecise, indicating the need for specific enhancements on MLLMs towards these abilities. We hope that our benchmark can encourage the research community to delve deeper to discover and enhance these untapped potentials of MLLMs. | https://openreview.net/pdf/5cb0fc8befab83d5b0fb1f3257587eb3a4243387.pdf |
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting | https://openreview.net/forum?id=JePfAI8fah | https://openreview.net/forum?id=JePfAI8fah | Yong Liu,Tengge Hu,Haoran Zhang,Haixu Wu,Shiyu Wang,Lintao Ma,Mingsheng Long | ICLR 2024,Spotlight | The recent boom of linear forecasting models questions the ongoing passion for architectural modifications of Transformer-based forecasters. These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp. However, Transformers are challenged in forecasting series with larger lookback windows due to performance degradation and computation explosion. Besides, the embedding for each temporal token fuses multiple variates that represent potential delayed events and distinct physical measurements, which may fail in learning variate-centric representations and result in meaningless attention maps. In this work, we reflect on the competent duties of Transformer components and repurpose the Transformer architecture without any modification to the basic components. We propose iTransformer that simply applies the attention and feed-forward network on the inverted dimensions. Specifically, the time points of individual series are embedded into variate tokens which are utilized by the attention mechanism to capture multivariate correlations; meanwhile, the feed-forward network is applied for each variate token to learn nonlinear representations. The iTransformer model achieves state-of-the-art on challenging real-world datasets, which further empowers the Transformer family with promoted performance, generalization ability across different variates, and better utilization of arbitrary lookback windows, making it a nice alternative as the fundamental backbone of time series forecasting. Code is available at this repository: https://github.com/thuml/iTransformer. | https://openreview.net/pdf/2dff92321d132ee1a70ba42c0046c99ee9bcd972.pdf |
De novo Protein Design Using Geometric Vector Field Networks | https://openreview.net/forum?id=9UIGyJJpay | https://openreview.net/forum?id=9UIGyJJpay | Weian Mao,Muzhi Zhu,Zheng Sun,Shuaike Shen,Lin Yuanbo Wu,Hao Chen,Chunhua Shen | ICLR 2024,Spotlight | Advances like protein diffusion have marked revolutionary progress in $\textit{de novo}$ protein design, a central topic in life science. These methods typically depend on protein structure encoders to model residue backbone frames, where atoms do not exist. Most prior encoders rely on atom-wise features, such as angles and distances between atoms, which are not available in this context. Only a few basic encoders, like IPA, have been proposed for this scenario, exposing the frame modeling as a bottleneck. In this work, we introduce the Vector Field Network (VFN), that enables network layers to perform learnable vector computations between coordinates of frame-anchored virtual atoms, thus achieving a higher capability for modeling frames. The vector computation operates in a manner similar to a linear layer, with each input channel receiving 3D virtual atom coordinates instead of scalar values. The multiple feature vectors output by the vector computation are then used to update the residue representations and virtual atom coordinates via attention aggregation. Remarkably, VFN also excels in modeling both frames and atoms, as the real atoms can be treated as the virtual atoms for modeling, positioning VFN as a potential $\textit{universal encoder}$. In protein diffusion (frame modeling), VFN exhibits a impressive performance advantage over IPA, excelling in terms of both designability ($\textbf{67.04}$\% vs. 53.58\%) and diversity ($\textbf{66.54}$\% vs. 51.98\%). In inverse folding(frame and atom modeling), VFN outperforms the previous SoTA model, PiFold ($\textbf{54.7}$\% vs. 51.66\%), on sequence recovery rate; we also propose a method of equipping VFN with the ESM model, which significantly surpasses the previous ESM-based SoTA ($\textbf{62.67}$\% vs. 55.65\%), LM-Design, by a substantial margin. Code is available at https://github.com/aim-uofa/VFN | https://openreview.net/pdf/88687a41b251ea3e5018907ebe06860c15e31ff3.pdf |
Prompt Gradient Projection for Continual Learning | https://openreview.net/forum?id=EH2O3h7sBI | https://openreview.net/forum?id=EH2O3h7sBI | Jingyang Qiao,zhizhong zhang,Xin Tan,Chengwei Chen,Yanyun Qu,Yong Peng,Yuan Xie | ICLR 2024,Spotlight | Prompt-tuning has demonstrated impressive performance in continual learning by querying relevant prompts for each input instance, which can avoid the introduction of task identifier. Its forgetting is therefore reduced as this instance-wise query mechanism enables us to select and update only relevant prompts. In this paper, we further integrate prompt-tuning with gradient projection approach. Our observation is: prompt-tuning releases the necessity of task identifier for gradient projection method; and gradient projection provides theoretical guarantees against forgetting for prompt-tuning. This inspires a new prompt gradient projection approach (PGP) for continual learning. In PGP, we deduce that reaching the orthogonal condition for prompt gradient can effectively prevent forgetting via the self-attention mechanism in vision-transformer. The condition equations are then realized by conducting Singular Value Decomposition (SVD) on an element-wise sum space between input space and prompt space. We validate our method on diverse datasets and experiments demonstrate the efficiency of reducing forgetting both in class incremental, online class incremental, and task incremental settings. The code is available at https://github.com/JingyangQiao/prompt-gradient-projection. | https://openreview.net/pdf/1328fe645bf366bf46fb88b51ebb5b08bc5e76f4.pdf |
R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning | https://openreview.net/forum?id=Si3YFA641c | https://openreview.net/forum?id=Si3YFA641c | Mengyuan Chen,Junyu Gao,Changsheng Xu | ICLR 2024,Spotlight | A newly-arising uncertainty estimation method named Evidential Deep Learning (EDL), which can obtain reliable predictive uncertainty in a single forward pass, has garnered increasing interest. Guided by the subjective logic theory, EDL obtains Dirichlet concentration parameters from deep neural networks, thus constructing a Dirichlet probability density function (PDF) to model the distribution of class probabilities. Despite its great success, we argue that EDL keeps nonessential settings in both stages of model construction and optimization.
In this work, our analysis indicates that (1) in the construction of the Dirichlet PDF, a commonly ignored parameter termed prior weight governs the balance between leveraging the proportion of evidence and its magnitude in deriving predictive scores, and (2) in model optimization, a variance-minimized regularization term adopted by traditional EDL encourages the Dirichlet PDF to approach a Dirac delta function, potentially exacerbating overconfidence. Therefore, we propose the R-EDL (Relaxed-EDL) method by relaxing these nonessential settings. Specifically, R-EDL treats the prior weight as an adjustable hyper-parameter instead of a fixed scalar, and directly optimizes the expectation of the Dirichlet PDF provided to deprecate the variance-minimized regularization term. Extensive experiments and SOTA performances demonstrate the effectiveness of our method. Source codes are provided in Appendix E. | https://openreview.net/pdf/34d74bb1c927a5db5a344d0e49273f94e775cacd.pdf |
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization | https://openreview.net/forum?id=SNGXbZtK6Q | https://openreview.net/forum?id=SNGXbZtK6Q | Yibing Liu,Chris XING TIAN,Haoliang Li,Lei Ma,Shiqi Wang | ICLR 2024,Spotlight | The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i.e., in-distribution (InD). In this paper, we study the OOD problem from a neuron activation view. We first formulate neuron activation states by considering both the neuron output and its influence on model decisions. Then, to characterize the relationship between neurons and OOD issues, we introduce the *neuron activation coverage* (NAC) -- a simple measure for neuron behaviors under InD data. Leveraging our NAC, we show that 1) InD and OOD inputs can be largely separated based on the neuron behavior, which significantly eases the OOD detection problem and beats the 21 previous methods over three benchmarks (CIFAR-10, CIFAR-100, and ImageNet-1K). 2) a positive correlation between NAC and model generalization ability consistently holds across architectures and datasets, which enables a NAC-based criterion for evaluating model robustness. Compared to prevalent InD validation criteria, we show that NAC not only can select more robust models, but also has a stronger correlation with OOD test performance. | https://openreview.net/pdf/4c57d3fc275b61826d7f559c23288a82feebda62.pdf |
ResFields: Residual Neural Fields for Spatiotemporal Signals | https://openreview.net/forum?id=EHrvRNs2Y0 | https://openreview.net/forum?id=EHrvRNs2Y0 | Marko Mihajlovic,Sergey Prokudin,Marc Pollefeys,Siyu Tang | ICLR 2024,Spotlight | Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP). However, despite the power and simplicity of representing signals with an MLP, these methods still face challenges when modeling large and complex temporal signals due to the limited capacity of MLPs. In this paper, we propose an effective approach to address this limitation by incorporating temporal residual layers into neural fields, dubbed ResFields. It is a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct a comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters and enhance generalization capabilities. Importantly, our formulation seamlessly integrates with existing MLP-based neural fields and consistently improves results across various challenging tasks: 2D video approximation, dynamic shape modeling via temporal SDFs, and dynamic NeRF reconstruction. Lastly, we demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse RGBD cameras of a lightweight capture system. | https://openreview.net/pdf/f19ba6990ec3698aae30dc2d3ca992a323a90cc3.pdf |
TD-MPC2: Scalable, Robust World Models for Continuous Control | https://openreview.net/forum?id=Oxh5CstDJU | https://openreview.net/forum?id=Oxh5CstDJU | Nicklas Hansen,Hao Su,Xiaolong Wang | ICLR 2024,Spotlight | TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series of improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves significantly over baselines across 104 online RL tasks spanning 4 diverse task domains, achieving consistently strong results with a single set of hyperparameters. We further show that agent capabilities increase with model and data size, and successfully train a single 317M parameter agent to perform 80 tasks across multiple task domains, embodiments, and action spaces. We conclude with an account of lessons, opportunities, and risks associated with large TD-MPC2 agents.
Explore videos, models, data, code, and more at https://tdmpc2.com | https://openreview.net/pdf/d7962b8e764953c9c4db96aa85307e9e9b689881.pdf |
Stochastic Controlled Averaging for Federated Learning with Communication Compression | https://openreview.net/forum?id=jj5ZjZsWJe | https://openreview.net/forum?id=jj5ZjZsWJe | Xinmeng Huang,Ping Li,Xiaoyun Li | ICLR 2024,Spotlight | Communication compression has been an important topic in Federated Learning (FL) for alleviating the communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs, building upon which we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover,SCALLION and SCAFCOM attain fast convergence rates under arbitrary data heterogeneity without any additional assumptions on compression errors. Experiments show that \scallion and \scafcom outperform recent compressed FL methods under the same communication budget. | https://openreview.net/pdf/b8db6edbb504d97145e9868b1a1f677abcd2f776.pdf |
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning | https://openreview.net/forum?id=Fx2SbBgcte | https://openreview.net/forum?id=Fx2SbBgcte | Yuwei Guo,Ceyuan Yang,Anyi Rao,Zhengyang Liang,Yaohui Wang,Yu Qiao,Maneesh Agrawala,Dahua Lin,Bo Dai | ICLR 2024,Spotlight | With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff. | https://openreview.net/pdf/f6573e09993ba1372e3b282b7d2b9d1b19cef04f.pdf |
Guiding Instruction-based Image Editing via Multimodal Large Language Models | https://openreview.net/forum?id=S1RKWSyZ2Y | https://openreview.net/forum?id=S1RKWSyZ2Y | Tsu-Jui Fu,Wenze Hu,Xianzhi Du,William Yang Wang,Yinfei Yang,Zhe Gan | ICLR 2024,Spotlight | Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, human instructions are sometimes too brief for current methods to capture and follow. Multimodal large language models (MLLMs) show promising capabilities in cross-modal understanding and visual-aware response generation via LMs. We investigate how MLLMs facilitate edit instructions and present MLLM-Guided Image Editing (MGIE). MGIE learns to derive expressive instructions and provides explicit guidance. The editing model jointly captures this visual imagination and performs manipulation through end-to-end training. We evaluate various aspects of Photoshop-style modification, global photo optimization, and local editing. Extensive experimental results demonstrate that expressive instructions are crucial to instruction-based image editing, and our MGIE can lead to a notable improvement in automatic metrics and human evaluation while maintaining competitive inference efficiency. | https://openreview.net/pdf/859bb6596377ab0632e753f95d8701a768c8a514.pdf |
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning | https://openreview.net/forum?id=YR3ETaElNK | https://openreview.net/forum?id=YR3ETaElNK | Bingchen Zhao,Haoqin Tu,Chen Wei,Jieru Mei,Cihang Xie | ICLR 2024,Spotlight | This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models.
By conceptualizing this transformation as a domain adaptation process, \ie, transitioning from text understanding to embracing multiple modalities, we intriguingly note that, within each attention block, tuning LayerNorm suffices to yield strong performance.
Moreover, when benchmarked against other tuning approaches like full parameter finetuning or LoRA, its benefits on efficiency are substantial.
For example, when compared to LoRA on a 13B model scale, performance can be enhanced by an average of over 20\% across five multi-modal tasks, and meanwhile,
results in a significant reduction of trainable parameters by 41.9\% and a decrease in GPU memory usage by 17.6\%. On top of this LayerNorm strategy, we showcase that selectively tuning only with conversational data can improve efficiency further.
Beyond these empirical outcomes, we provide a comprehensive analysis to explore the role of LayerNorm in adapting LLMs to the multi-modal domain and improving the expressive power of the model. | https://openreview.net/pdf/9ac467b9b8ce5db3006b34be33aadcefa332ff07.pdf |
Universal Humanoid Motion Representations for Physics-Based Control | https://openreview.net/forum?id=OrOd8PxOO2 | https://openreview.net/forum?id=OrOd8PxOO2 | Zhengyi Luo,Jinkun Cao,Josh Merel,Alexander Winkler,Jing Huang,Kris M. Kitani,Weipeng Xu | ICLR 2024,Spotlight | We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers their applicability in complex tasks. We close this gap by significantly increasing the coverage of our motion representation space. To achieve this, we first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator. This is achieved by using an encoder-decoder structure with a variational information bottleneck. Additionally, we jointly learn a prior conditioned on proprioception (humanoid's own pose and velocities) to improve model expressiveness and sampling efficiency for downstream tasks. By sampling from the prior, we can generate long, stable, and diverse human motions. Using this latent space for hierarchical RL, we show that our policies solve tasks using human-like behavior. We demonstrate the effectiveness of our motion representation by solving generative tasks (e.g. strike, terrain traversal) and motion tracking using VR controllers. | https://openreview.net/pdf/233f936dc4663dc0d31d5b53ea52bdc601f0b2f0.pdf |
Adaptive Rational Activations to Boost Deep Reinforcement Learning | https://openreview.net/forum?id=g90ysX1sVs | https://openreview.net/forum?id=g90ysX1sVs | Quentin Delfosse,Patrick Schramowski,Martin Mundt,Alejandro Molina,Kristian Kersting | ICLR 2024,Spotlight | Latest insights from biology show that intelligence not only emerges from the connections between neurons, but that individual neurons shoulder more computational responsibility than previously anticipated. Specifically, neural plasticity should be critical in the context of constantly changing reinforcement learning (RL) environments, yet current approaches still primarily employ static activation functions. In this work, we motivate the use of adaptable activation functions in RL and show that rational activation functions are particularly suitable for augmenting plasticity. Inspired by residual networks, we derive a condition under which rational units are closed under residual connections and formulate a naturally regularised version. The proposed joint-rational activation allows for desirable degrees of flexibility, yet regularises plasticity to an extent that avoids overfitting by leveraging a mutual set of activation function parameters across layers. We demonstrate that equipping popular algorithms with (joint) rational activations leads to consistent improvements on different games from the Atari Learning Environment benchmark, notably making DQN competitive to DDQN and Rainbow. | https://openreview.net/pdf/0ecc8520a8a62e463d88c1c7abc28fb1fc02ca9a.pdf |
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s) | https://openreview.net/forum?id=wISvONp3Kq | https://openreview.net/forum?id=wISvONp3Kq | Diyang Li,Charles Ling,zhiqiang xu,Huan Xiong,Bin Gu | ICLR 2024,Spotlight | Generalized Linear Models (GLMs) encompass a wide array of regression and classification models, where prediction is a function of a linear combination of the input variables. Often in real-world scenarios, a number of observations would be added into or removed from the existing training dataset, necessitating the development of learning systems that can efficiently train optimal models with varying observations in an online (sequential) manner instead of retraining from scratch. Despite the significance of data-varying scenarios, most existing approaches to sparse GLMs concentrate on offline batch updates, leaving online solutions largely underexplored. In this work, we present the first algorithm without compromising accuracy for GLMs regularized by sparsity-enforcing penalties trained on varying observations. Our methodology is capable of handling the addition and deletion of observations simultaneously, while adaptively updating data-dependent regularization parameters to ensure the best statistical performance. Specifically, we recast sparse GLMs as a bilevel optimization objective upon varying observations and characterize it as an explicit gradient flow in the underlying space for the inner and outer subproblems we are optimizing over, respectively. We further derive a set of rules to ensure a proper transition at regions of non-smoothness, and establish the guarantees of theoretical consistency and finite convergence. Encouraging results are exhibited on real-world benchmarks. | https://openreview.net/pdf/8a85e78845868ba7ca6a019bc980cb4360741379.pdf |
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game | https://openreview.net/forum?id=fsW7wJGLBd | https://openreview.net/forum?id=fsW7wJGLBd | Sam Toyer,Olivia Watkins,Ethan Adrian Mendes,Justin Svegliato,Luke Bailey,Tiffany Wang,Isaac Ong,Karim Elmaaroufi,Pieter Abbeel,Trevor Darrell,Alan Ritter,Stuart Russell | ICLR 2024,Spotlight | While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to *prompt injection attacks*: malicious third party prompts that subvert the intent of the system designer. To help researchers study this problem, we present a dataset of over 563,000 prompt injection attacks and 118,000 prompt-based "defenses" against prompt injection, all created by players of an online game called Tensor Trust. To the best of our knowledge, this is the first dataset that includes both human-generated attacks and defenses for instruction-following LLMs. The attacks in our dataset have easily interpretable structure, and shed light on the weaknesses of LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as *prompt extraction* and *prompt hijacking*. Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset. Furthermore, we show that some attack strategies from the dataset generalize to deployed LLM-based applications, even though they have a very different set of constraints to the game. We release data and code at [tensortrust.ai/paper](https://tensortrust.ai/paper) | https://openreview.net/pdf/abc69ff28e9d7de9a838f7c5d380e33bef63bc80.pdf |
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency | https://openreview.net/forum?id=kNjrhD67LP | https://openreview.net/forum?id=kNjrhD67LP | Tianhong Li,Sangnie Bhardwaj,Yonglong Tian,Han Zhang,Jarred Barber,Dina Katabi,Guillaume Lajoie,Huiwen Chang,Dilip Krishnan | ICLR 2024,Spotlight | Current vision-language generative models rely on expansive corpora of $\textit{paired}$ image-text data to attain optimal performance and generalization capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce $\textbf{ITIT}$ ($\textbf{I}$n$\textbf{T}$egrating $\textbf{I}$mage $\textbf{T}$ext): an innovative training paradigm grounded in the concept of cycle consistency which allows vision-language training on $\textit{unpaired}$ image and text data. ITIT is comprised of a joint image-text encoder with disjoint image and text decoders that enable bidirectional image-to-text and text-to-image generation in a single framework. During training, ITIT leverages a small set of paired image-text data to ensure its output matches the input reasonably well in both directions. Simultaneously, the model is also trained on much larger datasets containing only images or texts. This is achieved by enforcing cycle consistency between the original unpaired samples and the cycle-generated counterparts. For instance, it generates a caption for a given input image and then uses the caption to create an output image, and enforces similarity between the input and output images. Our experiments show that ITIT with unpaired datasets exhibits similar scaling behavior as using high-quality paired data. We demonstrate image generation and captioning performance on par with state-of-the-art text-to-image and image-to-text models with orders of magnitude fewer (only 3M) paired image-text data. Code will be released at https://github.com/LTH14/itit. | https://openreview.net/pdf/3962f9f3be59d06e85977b838e506fc5374735bf.pdf |
Learning the greatest common divisor: explaining transformer predictions | https://openreview.net/forum?id=cmcD05NPKa | https://openreview.net/forum?id=cmcD05NPKa | Francois Charton | ICLR 2024,Spotlight | The predictions of small transformers, trained to calculate the greatest common divisor (GCD) of two positive integers, can be fully characterized by looking at model inputs and outputs.
As training proceeds, the model learns a list $\mathcal D$ of integers, products of divisors of the base used to represent integers and small primes, and predicts the largest element of $\mathcal D$ that divides both inputs.
Training distributions impact performance. Models trained from uniform operands only learn a handful of GCD (up to $38$ GCD $\leq100$). Log-uniform operands boost performance to $73$ GCD $\leq 100$, and a log-uniform distribution of outcomes (i.e. GCD) to $91$. However, training from uniform (balanced) GCD breaks explainability. | https://openreview.net/pdf/d05e583ed331a78daf927ca6dbe517768d804505.pdf |
Space and time continuous physics simulation from partial observations | https://openreview.net/forum?id=4yaFQ7181M | https://openreview.net/forum?id=4yaFQ7181M | Steeven JANNY,Madiha Nadri,Julie Digne,Christian Wolf | ICLR 2024,Spotlight | Modern techniques for physical simulations rely on numerical schemes and mesh-refinement methods to address trade-offs between precision and complexity, but these handcrafted solutions are tedious and require high computational power. Data-driven methods based on large-scale machine learning promise high adaptivity by integrating long-range dependencies more directly and efficiently. In this work, we focus on computational fluid dynamics and address the shortcomings of a large part of the literature, which are based on fixed support for computations and predictions in the form of regular or irregular grids. We propose a novel setup to perform predictions in a continuous spatial and temporal domain while being trained on sparse observations. We formulate the task as a double observation problem and propose a solution with two interlinked dynamical systems defined on, respectively, the sparse positions and the continuous domain, which allows to forecast and interpolate a solution from the initial condition. Our practical implementation involves recurrent GNNs and a spatio-temporal attention observer capable of interpolating the solution at arbitrary locations. Our model not only generalizes to new initial conditions (as standard auto-regressive models do) but also performs evaluation at arbitrary space and time locations. We evaluate on three standard datasets in fluid dynamics and compare to strong baselines, which are outperformed in classical settings and the extended new task requiring continuous predictions. | https://openreview.net/pdf/1d8bb2f00597f5c08519af2931e6d36c1c80ddcf.pdf |
GROOT: Learning to Follow Instructions by Watching Gameplay Videos | https://openreview.net/forum?id=uleDLeiaT3 | https://openreview.net/forum?id=uleDLeiaT3 | Shaofei Cai,Bowei Zhang,Zihao Wang,Xiaojian Ma,Anji Liu,Yitao Liang | ICLR 2024,Spotlight | We study the problem of building a controller that can follow open-ended instructions in open-world environments. We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations. A new learning framework is derived to allow learning such instruction-following controllers from gameplay videos while producing a video instruction encoder that induces a structured goal space. We implement our agent GROOT in a simple yet effective encoder-decoder architecture based on causal transformers. We evaluate GROOT against open-world counterparts and human players on a proposed Minecraft SkillForge benchmark. The Elo ratings clearly show that GROOT is closing the human-machine gap as well as exhibiting a 70% winning rate over the best generalist agent baseline. Qualitative analysis of the induced goal space further demonstrates some interesting emergent properties, including the goal composition and complex gameplay behavior synthesis. | https://openreview.net/pdf/4da4eff07073a157b856755f8dfedb2c2e607c6b.pdf |
Mask-Based Modeling for Neural Radiance Fields | https://openreview.net/forum?id=SEiuSzlD1d | https://openreview.net/forum?id=SEiuSzlD1d | Ganlin Yang,Guoqiang Wei,Zhizheng Zhang,Yan Lu,Dong Liu | ICLR 2024,Spotlight | Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities,which restrict their applicability in representing multiple scenes using a single model. To address this problem, existing generalizable NeRF methods simply condition the model on image features. These methods still struggle to learn precise global representations over diverse scenes since they lack an effective mechanism for interacting among different points and views. In this work, we unveil that 3D implicit representation learning can be significantly improved by mask-based modeling. Specifically, we propose **m**asked **r**ay and **v**iew **m**odeling for generalizable **NeRF** (**MRVM-NeRF**), which is a self-supervised pretraining target to predict complete scene representations from partially masked features along each ray. With this pretraining target, MRVM-NeRF enables better use of correlations across different rays and views as the geometry priors, which thereby strengthens the capability of capturing intricate details within the scenes and boosts the generalization capability across different scenes. Extensive experiments demonstrate the effectiveness of our proposed MRVM-NeRF on both synthetic and real-world datasets, qualitatively and quantitatively. Besides, we also conduct experiments to show the compatibility of our proposed method with various backbones and its superiority under few-shot cases. | https://openreview.net/pdf/60aa67a90ce56bdf66d121eaf8fa35163fc77a76.pdf |
Large Language Models Are Not Robust Multiple Choice Selectors | https://openreview.net/forum?id=shr9PXz7T0 | https://openreview.net/forum?id=shr9PXz7T0 | Chujie Zheng,Hao Zhou,Fandong Meng,Jie Zhou,Minlie Huang | ICLR 2024,Spotlight | Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs). This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent “selection bias”, namely, they prefer to select specific option IDs as answers (like “Option A”). Through extensive empirical analyses with 20 LLMs on three benchmarks, we pinpoint that this behavioral bias primarily stems from LLMs’ token bias, where the model a priori assigns more probabilistic mass to specific option ID tokens (e.g., A/B/C/D) when predicting answers from the option IDs. To mitigate selection bias, we propose a label-free, inference-time debiasing method, called PriDe, which separates the model’s prior bias for option IDs from the overall prediction distribution. PriDe first estimates the prior by permutating option contents on a small number of test samples, and then applies the estimated prior to debias the remaining samples. We demonstrate that it achieves interpretable and transferable debiasing with high computational efficiency. We hope this work can draw broader research attention to the bias and robustness of modern LLMs. | https://openreview.net/pdf/e44715dd5477d90ebbb069b1ec6c7e18756295f5.pdf |
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula | https://openreview.net/forum?id=pFOoOdaiue | https://openreview.net/forum?id=pFOoOdaiue | Aryaman Reddi,Maximilian Tölle,Jan Peters,Georgia Chalvatzaki,Carlo D'Eramo | ICLR 2024,Spotlight | Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i.e., rational strategy, corresponds to a Nash equilibrium. However, finding Nash equilibria requires facing complex saddle point optimization problems, which can be prohibitive to solve, especially for high-dimensional control. In this paper, we propose a novel approach for adversarial RL based on entropy regularization to ease the complexity of the saddle point optimization problem. We show that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality, i.e., agents sometimes play random actions instead of optimal ones. Crucially, the connection between the entropy-regularized objective and QRE enables free modulation of the rationality of the agents by simply tuning the temperature coefficient. We leverage this insight to propose our novel algorithm, Quantal Adversarial RL (QARL), which gradually increases the rationality of the adversary in a curriculum fashion until it is fully rational, easing the complexity of the optimization problem while retaining robustness. We provide extensive evidence of QARL outperforming RARL and recent baselines across several MuJoCo locomotion and navigation problems in overall performance and robustness. | https://openreview.net/pdf/8a7e2e74cb77bfa87eaf438766767c4f2fc26a18.pdf |
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions | https://openreview.net/forum?id=BXY6fe7q31 | https://openreview.net/forum?id=BXY6fe7q31 | Juncheng Li,Kaihang Pan,Zhiqi Ge,Minghe Gao,Wei Ji,Wenqiao Zhang,Tat-Seng Chua,Siliang Tang,Hanwang Zhang,Yueting Zhuang | ICLR 2024,Spotlight | Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can recognize. This is achieved by training the VPGs on millions of image-caption pairs, where the VPG-generated tokens of images are fed into a frozen LLM to generate the corresponding captions. However, this image-captioning based training objective inherently biases the VPG to concentrate solely on the primary visual contents sufficient for caption generation, often neglecting other visual details. This shortcoming results in MLLMs’ underperformance in comprehending demonstrative instructions consisting of multiple, interleaved, and multimodal instructions that demonstrate the required context to complete a task. To address this issue, we introduce a generic and lightweight Visual Prompt Generator Complete module (VPG-C), which can infer and complete the missing details essential for comprehending demonstrative instructions. Further, we propose a synthetic discriminative training strategy to fine-tune VPG-C, eliminating the need for supervised demonstrative instructions. As for evaluation, we build DEMON, a comprehensive benchmark for demonstrative instruction understanding. Synthetically trained with the proposed strategy, VPG-C achieves significantly stronger zero-shot performance across all tasks of DEMON. Further evaluation on the MME and OwlEval benchmarks also demonstrate the superiority of VPG-C. The code and models are available at https://github.com/DCDmllm/Cheetah. | https://openreview.net/pdf/2c2260fc62d6a180e12e943725968e430205fe0a.pdf |
CLAP: Collaborative Adaptation for Patchwork Learning | https://openreview.net/forum?id=8EyRkd3Qj2 | https://openreview.net/forum?id=8EyRkd3Qj2 | Sen Cui,Abudukelimu Wuerkaixi,Weishen Pan,Jian Liang,Lei Fang,Changshui Zhang,Fei Wang | ICLR 2024,Spotlight | In this paper, we investigate a new practical learning scenario, where the data distributed in different sources/clients are typically generated with various modalities. Existing research on learning from multi-source data mostly assume that each client owns the data of all modalities, which may largely limit its practicability. In light of the expensiveness and sparsity of multimodal data, we propose patchwork learning to jointly learn from fragmented multimodal data in distributed clients. Considering the concerns on data privacy, patchwork learning aims to impute incomplete multimodal data for diverse downstream tasks without accessing the raw data directly. Local clients could miss different modality combinations. Due to the statistical heterogeneity induced by non-i.i.d. data, the imputation is more challenging since the learned dependencies fail to adapt to the imputation of other clients. In this paper, we provide a novel imputation framework to tackle modality combination heterogeneity and statistical heterogeneity simultaneously, called ``collaborative adaptation''. In particular, for two observed modality combinations from two clients, we learn the transformations between their maximal intersection and other modalities by proposing a novel ELBO. We improve the worst-performing required transformations through a Pareto min-max optimization framework. In extensive experiments, we demonstrate the superiority of the proposed method compared to existing related methods on benchmark data sets and a real-world clinical data set. | https://openreview.net/pdf/aef23787e155a9467c38a494226d5c7a499c0fec.pdf |
Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework | https://openreview.net/forum?id=eoSeaK4QJo | https://openreview.net/forum?id=eoSeaK4QJo | Xinyu Shi,Jianhao Ding,Zecheng Hao,Zhaofei Yu | ICLR 2024,Spotlight | Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have demonstrated the impressive performance of deep SNNs on challenging tasks, their energy efficiency advantage has been diminished. Existing methods targeting energy consumption reduction do not fully exploit sparsity, whereas powerful pruning methods can achieve high sparsity but are not directly targeted at energy efficiency, limiting their effectiveness in energy saving. Furthermore, none of these works fully exploit the sparsity of neurons or the potential for unstructured neuron pruning in SNNs. In this paper, we propose a novel pruning framework that combines unstructured weight pruning with unstructured neuron pruning to maximize the utilization of the sparsity of neuromorphic computing, thereby enhancing energy efficiency. To the best of our knowledge, this is the first application of unstructured neuron pruning to deep SNNs. Experimental results demonstrate that our method achieves impressive energy efficiency gains. The sparse network pruned by our method with only 0.63\% remaining connections can achieve a remarkable 91 times increase in energy efficiency compared to the original dense network, requiring only 8.5M SOPs for inference, with merely 2.19\% accuracy loss on the CIFAR-10 dataset. Our work suggests that deep and dense SNNs exhibit high redundancy in energy consumption, highlighting the potential for targeted SNN sparsification to save energy. | https://openreview.net/pdf/0d2fdc7d0fd2120025beecf584d1633bbeebad5e.pdf |
Online Stabilization of Spiking Neural Networks | https://openreview.net/forum?id=CIj1CVbkpr | https://openreview.net/forum?id=CIj1CVbkpr | Yaoyu Zhu,Jianhao Ding,Tiejun Huang,Xiaodong Xie,Zhaofei Yu | ICLR 2024,Spotlight | Spiking neural networks (SNNs), attributed to the binary, event-driven nature of spikes, possess heightened biological plausibility and enhanced energy efficiency on neuromorphic hardware compared to analog neural networks (ANNs). Mainstream SNN training schemes apply backpropagation-through-time (BPTT) with surrogate gradients to replace the non-differentiable spike emitting process during backpropagation. While achieving competitive performance, the requirement for storing intermediate information at all time-steps incurs higher memory consumption and fails to fulfill the online property crucial to biological brains.
Our work focuses on online training techniques, aiming for memory efficiency while preserving biological plausibility.
The limitation of not having access to future information in early time steps in online training has constrained previous efforts to incorporate advantageous modules such as batch normalization.
To address this problem, we propose Online Spiking Renormalization (OSR) to ensure consistent parameters between testing and training, and Online Threshold Stabilizer (OTS) to stabilize neuron firing rates across time steps. Furthermore, we design a novel online approach to compute the sample mean and variance over time for OSR. Experiments conducted on various datasets demonstrate the proposed method's superior performance among SNN online training algorithms.
Our code is available at https://github.com/zhuyaoyu/SNN-online-normalization. | https://openreview.net/pdf/661b6685d85268b6527ce98af4c631b5df5b121d.pdf |
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping | https://openreview.net/forum?id=3M0GXoUEzP | https://openreview.net/forum?id=3M0GXoUEzP | Tim Lebailly,Thomas Stegmüller,Behzad Bozorgtabar,Jean-Philippe Thiran,Tinne Tuytelaars | ICLR 2024,Spotlight | Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images. However, this approach faces limitations when applied to scene-centric datasets, where multiple objects within an image are only implicitly captured in the global representation. Such global bootstrapping can lead to undesirable entanglement of object representations. Furthermore, even object-centric datasets stand to benefit from a finer-grained bootstrapping approach. In response to these challenges, we introduce a novel $\textbf{Cr}$oss-$\textbf{I}$mage Object-Level $\textbf{Bo}$otstrapping method tailored to enhance dense visual representation learning. By employing object-level nearest neighbor bootstrapping throughout the training, CrIBo emerges as a notably strong and adequate candidate for in-context learning, leveraging nearest neighbor retrieval at test time. CrIBo shows state-of-the-art performance on the latter task while being highly competitive in more standard downstream segmentation tasks. Our code and pretrained models are publicly available at https://github.com/tileb1/CrIBo. | https://openreview.net/pdf/90f302e1380ba1015e1c1a8508286a9ec74ca3b1.pdf |
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis | https://openreview.net/forum?id=7ERQPyR2eb | https://openreview.net/forum?id=7ERQPyR2eb | Zhenhui Ye,Tianyun Zhong,Yi Ren,Jiaqi Yang,Weichuang Li,Jiawei Huang,Ziyue Jiang,Jinzheng He,Rongjie Huang,Jinglin Liu,Chen Zhang,Xiang Yin,Zejun MA,Zhou Zhao | ICLR 2024,Spotlight | One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video. The existing methods fail to simultaneously achieve the goals of accurate 3D avatar reconstruction and stable talking face animation. Besides, while the existing works mainly focus on synthesizing the head part, it is also vital to generate natural torso and background segments to obtain a realistic talking portrait video. To address these limitations, we present Real3D-Potrait, a framework that (1) improves the one-shot 3D reconstruction power with a large image-to-plane model that distills 3D prior knowledge from a 3D face generative model; (2) facilitates accurate motion-conditioned animation with an efficient motion adapter; (3) synthesizes realistic video with natural torso movement and switchable background using a head-torso-background super-resolution model; and (4) supports one-shot audio-driven talking face generation with a generalizable audio-to-motion model. Extensive experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos compared to previous methods. Video samples are available at https://real3dportrait.github.io. | https://openreview.net/pdf/c59a7b868258573cafe7e9a84243e3f096008b51.pdf |
TabR: Tabular Deep Learning Meets Nearest Neighbors | https://openreview.net/forum?id=rhgIgTSSxW | https://openreview.net/forum?id=rhgIgTSSxW | Yury Gorishniy,Ivan Rubachev,Nikolay Kartashev,Daniil Shlenskii,Akim Kotelnikov,Artem Babenko | ICLR 2024,Poster | Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers.
However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems.
One of the research directions aimed at improving the position of tabular DL involves designing so-called retrieval-augmented models.
For a target object, such models retrieve other objects (e.g. the nearest neighbors) from the available training data and use their features and labels to make a better prediction.
In this work, we present TabR -- essentially, a feed-forward network with a custom k-Nearest-Neighbors-like component in the middle.
On a set of public benchmarks with datasets up to several million objects, TabR marks a big step forward for tabular DL: it demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed "GBDT-friendly" benchmark (see Figure 1).
Among the important findings and technical details powering TabR, the main ones lie in the attention-like mechanism that is responsible for retrieving the nearest neighbors and extracting valuable signal from them.
In addition to the higher performance, TabR is simple and significantly more efficient compared to prior retrieval-based tabular DL models. | https://openreview.net/pdf/178e173a880d7872c0a79d88e005426c20501329.pdf |
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models | https://openreview.net/forum?id=qBL04XXex6 | https://openreview.net/forum?id=qBL04XXex6 | Sijia Chen,Baochun Li,Di Niu | ICLR 2024,Poster | The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of exploration and self-evaluation in reasoning step selection for complex problem solving. In this paper, we present Boosting of Thoughts (BoT), an automated prompting framework for problem solving with LLMs by iteratively exploring and self-evaluating many trees of thoughts in order to acquire an ensemble of trial-and-error reasoning experiences, which will serve as a new form of prompting to solve the complex problem. Starting from a simple prompt without requiring examples, BoT iteratively explores and evaluates a large collection of reasoning steps, and more importantly, uses error analysis obtained from the LLM on them to explicitly revise prompting, which in turn enhances reasoning step generation, until a final answer is attained. Our experiments with GPT-4 and Llama2 across extensive complex mathematical problems demonstrate that BoT consistently achieves higher or comparable problem-solving rates than other advanced prompting approaches. | https://openreview.net/pdf/a30673a601700226be14c851d887cc7181f4c78f.pdf |
Locality Sensitive Sparse Encoding for Learning World Models Online | https://openreview.net/forum?id=i8PjQT3Uig | https://openreview.net/forum?id=i8PjQT3Uig | Zichen Liu,Chao Du,Wee Sun Lee,Min Lin | ICLR 2024,Poster | Acquiring an accurate world model $\textit{online}$ for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online learning perspective, a Follow-The-Leader (FTL) world model is desirable, which optimally fits all previous experiences at each round. Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents. In this paper, we revisit models that can achieve FTL with incremental updates. Specifically, our world model is a linear regression model supported by nonlinear random features. The linear part ensures efficient FTL update while the nonlinear random feature empowers the fitting of complex environments. To best trade off model capacity and computation efficiency, we introduce a locality sensitive sparse encoding, which allows us to conduct efficient sparse updates even with very high dimensional nonlinear features. We validate the representation power of our encoding and verify that it allows efficient online learning under data covariate shift. We also show, in the Dyna MBRL setting, that our world models learned online using a $\textit{single pass}$ of trajectory data either surpass or match the performance of deep world models trained with replay and other continual learning methods. | https://openreview.net/pdf/0149a804bccf18bec867875f9e9fb664c4288f4b.pdf |
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations | https://openreview.net/forum?id=eepoE7iLpL | https://openreview.net/forum?id=eepoE7iLpL | Binghui Xie,Yatao Bian,Kaiwen Zhou,Yongqiang Chen,Peilin Zhao,Bo Han,Wei Meng,James Cheng | ICLR 2024,Poster | Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that capture the relationship between utility function values and subsets within their respective supersets. However, these approaches tend to overlook the valuable information contained within the superset when utilizing neural networks to model set functions. In this work, we address this oversight by adopting a probabilistic perspective. Our theoretical findings demonstrate that when the target value is conditioned on both the input set and subset, it is essential to incorporate an invariant sufficient statistic of the superset into the subset of interest for effective learning. This ensures that the output value remains invariant to permutations of the subset and its corresponding superset, enabling identification of the specific superset from which the subset originated. Motivated by these insights, we propose a simple yet effective information aggregation module designed to merge the representations of subsets and supersets from a permutation invariance perspective. Comprehensive empirical evaluations across diverse tasks and datasets validate the enhanced efficacy of our approach over conventional methods, underscoring the practicality and potency of our proposed strategies in real-world contexts. | https://openreview.net/pdf/c23dcae9832d01a0cbe616d94729f6b4a7c8365c.pdf |
Bridging Vision and Language Spaces with Assignment Prediction | https://openreview.net/forum?id=lK2V2E2MNv | https://openreview.net/forum?id=lK2V2E2MNv | Jungin Park,Jiyoung Lee,Kwanghoon Sohn | ICLR 2024,Poster | This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms the embedding space of pretrained vision models into the LLMs' word embedding space using a single linear layer for efficient and general-purpose visual and language understanding. Specifically, we harness well-established word embeddings to bridge two modality embedding spaces. The visual and text representations are simultaneously assigned to a set of word embeddings within pretrained LLMs by formulating the assigning procedure as an optimal transport problem. We predict the assignment of one modality from the representation of another modality data, enforcing consistent assignments for paired multimodal data. This allows vision and language representations to contain the same information, grounding the frozen LLMs' word embedding space in visual data. Moreover, a robust semantic taxonomy of LLMs can be preserved with visual data since the LLMs interpret and reason linguistic information from correlations between word embeddings. Experimental results show that VLAP achieves substantial improvements over the previous linear transformation-based approaches across a range of vision-language tasks, including image captioning, visual question answering, and cross-modal retrieval. We also demonstrate the learned visual representations hold a semantic taxonomy of LLMs, making visual semantic arithmetic possible. | https://openreview.net/pdf/f92c528b5ee0113caeb927bb8305b6afaaff0004.pdf |
Generative Judge for Evaluating Alignment | https://openreview.net/forum?id=gtkFw6sZGS | https://openreview.net/forum?id=gtkFw6sZGS | Junlong Li,Shichao Sun,Weizhe Yuan,Run-Ze Fan,hai zhao,Pengfei Liu | ICLR 2024,Poster | The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with human needs (e.g., brainstorming and email writing). This shift in task distribution imposes new requirements on evaluating these aligned models regarding *generality* (i.e., assessing performance across diverse scenarios), *flexibility* (i.e., examining under different protocols), and *interpretability* (i.e., scrutinizing models with explanations). In this paper, we propose a generative judge with 13B parameters, **Auto-J**, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios and accommodates diverse evaluation protocols (e.g., pairwise response comparison and single-response evaluation) with well-structured natural language critiques. To demonstrate the efficacy of our approach, we construct a new testbed covering 58 different scenarios. Experimentally, **Auto-J** outperforms a series of strong competitors, including both open-source and closed-source models, by a large margin. We also provide detailed analysis and case studies to further reveal the potential of our method and make a variety of resources public at https://github.com/GAIR-NLP/auto-j. | https://openreview.net/pdf/7fe3087c6257d9121061bc6f3cb0571abebf9277.pdf |
Rethinking and Extending the Probabilistic Inference Capacity of GNNs | https://openreview.net/forum?id=7vVWiCrFnd | https://openreview.net/forum?id=7vVWiCrFnd | Tuo Xu,Lei Zou | ICLR 2024,Poster | Designing expressive Graph Neural Networks (GNNs) is an important topic in graph machine learning fields. Despite the existence of numerous approaches proposed to enhance GNNs based on Weisfeiler-Lehman (WL) tests, what GNNs can and cannot learn still lacks a deeper understanding. This paper adopts a fundamentally different approach to examine the expressive power of GNNs from a probabilistic perspective. By establishing connections between GNNs' predictions and the central inference problems of probabilistic graphical models (PGMs), we can analyze previous GNN variants with a novel hierarchical framework and gain new insights into their node-level and link-level behaviors. Additionally, we introduce novel methods that can provably enhance GNNs' ability to capture complex dependencies and make complex predictions. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approaches. | https://openreview.net/pdf/20a211772e90fa923b10b62f56acff93f0b25cef.pdf |
Learning model uncertainty as variance-minimizing instance weights | https://openreview.net/forum?id=bDWXhzZT40 | https://openreview.net/forum?id=bDWXhzZT40 | Nishant Jain,Karthikeyan Shanmugam,Pradeep Shenoy | ICLR 2024,Poster | Predictive uncertainty--a model’s self-awareness regarding its accuracy on an input--is key for both building robust models via training interventions and for test-time applications such as selective classification. We propose a novel instance-conditional reweighting approach that captures predictive uncertainty using an auxiliary network, and unifies these train- and test-time applications. The auxiliary network is trained using a meta-objective in a bilevel optimization framework. A key contribution of our proposal is the meta-objective of minimizing dropout variance, an approximation of Bayesian predictive uncertainty, We show in controlled experiments that we effectively capture diverse specific notions of uncertainty through this meta-objective, while previous approaches only capture certain aspects. These results translate to significant gains in real-world settings–selective classification, label noise, domain adaptation, calibration–and across datasets–Imagenet, Cifar100, diabetic retinopathy, Camelyon, WILDs, Imagenet-C,-A,-R, Clothing-1.6M, etc. For Diabetic Retinopathy, we see upto 3.4\%/3.3\% accuracy & AUC gains over SOTA in selective classification. We also improve upon large-scale pretrained models such as PLEX. | https://openreview.net/pdf/ebdd64cc233d279eff550647eb57b1baf57fd9eb.pdf |
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization | https://openreview.net/forum?id=My7lkRNnL9 | https://openreview.net/forum?id=My7lkRNnL9 | Ravi Francesco Srinivasan,Francesca Mignacco,Martino Sorbaro,Maria Refinetti,Avi Cooper,Gabriel Kreiman,Giorgia Dellaferrera | ICLR 2024,Poster | "Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first address compelling challenges related to the "forward-only" rules, which include reducing the performance gap with backpropagation and providing an analytical understanding of their dynamics. To this end, we show that the forward-only algorithm with top-down feedback is well-approximated by an "adaptive-feedback-alignment" algorithm, and we analytically track its performance during learning in a prototype high-dimensional setting. Then, we compare different versions of forward-only algorithms, focusing on the Forward-Forward and PEPITA frameworks, and we show that they share the same learning principles. Overall, our work unveils the connections between three key neuro-inspired learning rules, providing a link between "forward-only" algorithms, i.e., Forward-Forward and PEPITA, and an approximation of backpropagation, i.e., Feedback Alignment. | https://openreview.net/pdf/d2749ae05700db2eaae75a159874c2e229ae8222.pdf |
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning | https://openreview.net/forum?id=AZGIwqCyYY | https://openreview.net/forum?id=AZGIwqCyYY | Yeongwoo Song,Hawoong Jeong | ICLR 2024,Poster | Recent advances in deep learning for physics have focused on discovering shared representations of target systems by incorporating physics priors or inductive biases into neural networks. While effective, these methods are limited to the system domain, where the type of system remains consistent and thus cannot ensure the adaptation to new, or unseen physical systems governed by different laws. For instance, a neural network trained on a mass-spring system cannot guarantee accurate predictions for the behavior of a two-body system or any other system with different physical laws.
In this work, we take a significant leap forward by targeting cross domain generalization within the field of Hamiltonian dynamics.
We model our system with a graph neural network (GNN) and employ a meta learning algorithm to enable the model to gain experience over a distribution of systems and make it adapt to new physics. Our approach aims to learn a unified Hamiltonian representation that is generalizable across multiple system domains, thereby overcoming the limitations of system-specific models.
We demonstrate that the meta-trained model captures the generalized Hamiltonian representation that is consistent across different physical domains.
Overall, through the use of meta learning, we offer a framework that achieves cross domain generalization, providing a step towards a unified model for understanding a wide array of dynamical systems via deep learning. | https://openreview.net/pdf/b18ec7715410bb144074f86aecfa3acfa91b52d9.pdf |
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning | https://openreview.net/forum?id=BTKAeLqLMw | https://openreview.net/forum?id=BTKAeLqLMw | Wei Liu,Weihao Zeng,Keqing He,Yong Jiang,Junxian He | ICLR 2024,Poster | Instruction tuning is a standard technique employed to align large language models to end tasks and user preferences after the initial pretraining phase. Recent research indicates the critical role of data engineering in instruction tuning -- when appropriately selected, only limited data is necessary to achieve superior performance. However, we still lack a principled understanding of what makes good instruction tuning data for alignment, and how we should select data automatically and effectively. In this work, we delve deeply into automatic data selection strategies for alignment. We start with controlled studies to measure data across three dimensions: complexity, quality, and diversity, along which we examine existing methods and introduce novel techniques for enhanced data measurement. Subsequently, we propose a simple strategy to select data samples based on the measurement. We present Deita (short for Data-Efficient Instruction Tuning for Alignment), a series of models fine-tuned from LLaMA models using data samples automatically selected with our proposed approach. When assessed through both automatic metrics and human evaluation, Deita performs better or on par with the state-of-the-art open-source alignment models such as Vicuna and WizardLM with only 6K training data samples -- 10x less than the data used in the baselines. We anticipate this work to provide clear guidelines and tools on automatic data selection, aiding researchers and practitioners in achieving data-efficient alignment. | https://openreview.net/pdf/a617374cea846fefdd6511494d4f9722b000a237.pdf |
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks | https://openreview.net/forum?id=AJBkfwXh3u | https://openreview.net/forum?id=AJBkfwXh3u | Kesen Zhao,Liang Zhang | ICLR 2024,Poster | Dynamic Graph Neural Networks (DyGNNs) have gained significant popularity in the research of dynamic graphs, but are limited by the low transparency, such that human-understandable insights can hardly be drawn from their predictions. Although a number of existing research have been devoted to investigating the interpretability of graph neural networks (GNNs), achieving the interpretability of DyGNNs is pivotally challenging due to the complex spatial-temporal correlations in dynamic graphs. To this end, we propose an innovative causality-inspired generative model based on structural causal model (SCM), which explores the underlying philosophies of DyGNN predictions by identifying the trivial, static, and dynamic causal relationships. To reach this goal, two critical tasks need to be accomplished including (1) disentangling the complex causal relationships, and (2) fitting the spatial-temporal explanations of DyGNNs in the SCM architecture. To tackle these challenges, the proposed method incorporates a contrastive learning module to disentangle trivial and causal relationships, and a dynamic correlating module to disentangle dynamic and static causal relationships, respectively. A dynamic VGAE-based framework is further developed, which generates causal-and-dynamic masks for spatial interpretability, and recognizes dynamic relationships along the time horizon through causal invention for temporal interpretability. Comprehensive experiments have been conducted on both synthetic and real-world datasets, where our approach yields substantial improvements, thereby demonstrating significant superiority. | https://openreview.net/pdf/da061144568aad672fd37e90edc5b87a39b91ba9.pdf |
Dissecting learning and forgetting in language model finetuning | https://openreview.net/forum?id=tmsqb6WpLz | https://openreview.net/forum?id=tmsqb6WpLz | Xiao Zhang,Ji Wu | ICLR 2024,Poster | Finetuning language models on domain-specific corpus is a common approach to enhance their domain knowledge and capability. While improving performance on domain tasks, it often brings a side-effect of forgetting of the model's general abilities. In this study, we analyze the effects of finetuning on language models by dissecting its impacts on the modeling of topic, style, and factual knowledge in text. Our method uses instruction-following LLMs such as ChatGPT to auto-generate controlled-variable text examples which we use to probe the model. Our findings reveal that finetuning results in significant shifts in the language model's topic and style priors, while actual knowledge learning only contributes to a small fraction of the total probability change. Analysis shows that the adaptation of topic and style priors behave akin to learning simple features: they are learned rapidly and require little model capacity. They are also learned independently and primarily at the beginning of a text sequence. In contrast, factual knowledge is learned stably but slowly and requires significant model capacity to learn. The research offers insights and understanding into the finer dynamics of learning and forgetting in language models, and can potentially inform future research on improving domain adaptation and addressing the challenges of forgetting in continual learning of language models. | https://openreview.net/pdf/11ec6e4f64662107e73511797d5b3135c9385ef5.pdf |
Test-time Adaptation against Multi-modal Reliability Bias | https://openreview.net/forum?id=TPZRq4FALB | https://openreview.net/forum?id=TPZRq4FALB | Mouxing Yang,Yunfan Li,Changqing Zhang,Peng Hu,Xi Peng | ICLR 2024,Poster | Test-time adaptation (TTA) has emerged as a new paradigm for reconciling distribution shifts across domains without accessing source data. However, existing TTA methods mainly concentrate on uni-modal tasks, overlooking the complexity of multi-modal scenarios.
In this paper, we delve into the multi-modal test-time adaptation and reveal a new challenge named reliability bias. Different from the definition of traditional distribution shifts, reliability bias refers to the information discrepancies across different modalities derived from intra-modal distribution shifts. To solve the challenge, we propose a novel method, dubbed REliable fusion and robust ADaptation (READ). On the one hand, unlike the existing TTA paradigm that mainly repurposes the normalization layers, READ employs a new paradigm that modulates the attention between modalities in a self-adaptive way, supporting reliable fusion against reliability bias. On the other hand, READ adopts a novel objective function for robust multi-modal adaptation, where the contributions of confident predictions could be amplified and the negative impacts of noisy predictions could be mitigated. Moreover, we introduce two new benchmarks to facilitate comprehensive evaluations of multi-modal TTA under reliability bias. Extensive experiments on the benchmarks verify the effectiveness of our method against multi-modal reliability bias. The code and benchmarks are available at https://github.com/XLearning-SCU/2024-ICLR-READ. | https://openreview.net/pdf/b4995bcc3ee4d649705147591c205b904f4a9d13.pdf |
Mirage: Model-agnostic Graph Distillation for Graph Classification | https://openreview.net/forum?id=78iGZdqxYY | https://openreview.net/forum?id=78iGZdqxYY | Mridul Gupta,Sahil Manchanda,HARIPRASAD KODAMANA,Sayan Ranu | ICLR 2024,Poster | GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an effort in that direction with the aim to construct a smaller synthetic training set from the original training data without significantly compromising model performance. While initial efforts are promising, this work is motivated by two key observations: (1) Existing graph distillation algorithms themselves rely on training with the full dataset, which undermines the very premise of graph distillation. (2) The distillation process is specific to the target GNN architecture and hyper-parameters and thus not robust to changes in the modeling pipeline. We circumvent these limitations by designing a distillation algorithm called MIRAGE for graph classification. MIRAGE is built on the insight that a message-passing GNN decomposes the input graph into a multiset of computation trees. Furthermore, the frequency distribution of computation trees is often skewed in nature, enabling us to condense this data into a concise distilled summary. By compressing the computation data itself, as opposed to emulating gradient flows on the original training set—a prevalent approach to date—MIRAGE transforms into an unsupervised and architecture-agnostic distillation algorithm. Extensive benchmarking on real-world datasets underscores MIRAGE’s superiority, showcasing enhanced generalization accuracy, data compression, and distillation efficiency when compared to state-of-the-art baselines. | https://openreview.net/pdf/6734ad0e165c7017f3c6c0f400183a1bae684654.pdf |
On the Learnability of Watermarks for Language Models | https://openreview.net/forum?id=9k0krNzvlV | https://openreview.net/forum?id=9k0krNzvlV | Chenchen Gu,Xiang Lisa Li,Percy Liang,Tatsunori Hashimoto | ICLR 2024,Poster | Watermarking of language model outputs enables statistical detection of model-generated text, which can mitigate harms and misuses of language models. Existing watermarking strategies operate by altering the decoder of an existing language model. In this paper, we ask whether language models can directly learn to generate watermarked text, which would have significant implications for the real-world deployment of watermarks. First, learned watermarks could be used to build open models that naturally generate watermarked text, enabling watermarking for open models, where users can control the decoding procedure. Second, if watermarking is used to determine the provenance of generated text, an adversary can hurt the reputation of a victim model by spoofing its watermark and generating damaging watermarked text. To investigate the learnability of watermarks, we propose watermark distillation, which trains a student model to behave like a teacher model that uses decoding-based watermarking. We test our approach on three decoding-based watermarking strategies and various hyperparameter settings, finding that models can learn to generate watermarked text with high detectability. We also find limitations to learnability, including the loss of watermarking capabilities under fine-tuning on normal text and high sample complexity when learning low-distortion watermarks. | https://openreview.net/pdf/218fcaae4d72e8c622881df1415344813b0aa3f0.pdf |
Bellman Optimal Stepsize Straightening of Flow-Matching Models | https://openreview.net/forum?id=Iyve2ycvGZ | https://openreview.net/forum?id=Iyve2ycvGZ | Bao Nguyen,Binh Nguyen,Viet Anh Nguyen | ICLR 2024,Poster | Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these models, especially during the finetuning process and sampling processes, pose significant challenges for low-resource scenarios. This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint. First, this technique involves a dynamic programming algorithm that optimizes the stepsizes of the pretrained network. Then, it refines the velocity network to match the optimal step sizes, aiming to straighten the generation paths. Extensive experimental evaluations across image generation tasks demonstrate the efficacy of BOSS in terms of both resource utilization and image quality. Our results reveal that BOSS achieves substantial gains in efficiency while maintaining competitive sample quality, effectively bridging the gap between low-resource constraints and the demanding requirements of flow-matching generative models. Our paper also fortifies the responsible development of artificial intelligence, offering a more sustainable generative model that reduces computational costs and environmental footprints. Our code can be found at https://github.com/nguyenngocbaocmt02/BOSS. | https://openreview.net/pdf/49149ffea0255c53a80d017478e79f91b0a6c402.pdf |
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction | https://openreview.net/forum?id=6ARlSgun7J | https://openreview.net/forum?id=6ARlSgun7J | Anirudh Buvanesh,Rahul Chand,Jatin Prakash,Bhawna Paliwal,Mudit Dhawan,Neelabh Madan,Deepesh Hada,Vidit Jain,SONU MEHTA,Yashoteja Prabhu,Manish Gupta,Ramachandran Ramjee,Manik Varma | ICLR 2024,Poster | Extreme Classification (XC) architectures, which utilize a massive One-vs-All (OvA) classifier layer at the output, have demonstrated remarkable performance on problems with large label sets. Nonetheless, these architectures falter on tail labels with few representative samples. This phenomenon has been attributed to factors such as classifier over-fitting and missing label bias, and solutions involving regularization and loss re-calibration have been developed. This paper explores the impact of label variance - a previously unexamined factor - on the tail performance in extreme classifiers. It also develops a method to systematically reduce label variance in XC by transferring the knowledge from a specialized tail-robust teacher model to the OvA classifiers. For this purpose, it proposes a principled knowledge distillation framework, LEVER, which enhances the tail performance in extreme classifiers with formal guarantees on generalization. Comprehensive experiments are conducted on a diverse set of XC datasets, demonstrating that LEVER can enhance tail performance by around 5\% and 6\% points in PSP and coverage metrics, respectively, when integrated with leading extreme classifiers. Moreover, it establishes a new state-of-the-art when added to the top-performing Renee classifier. Extensive ablations and analyses substantiate the efficacy of our design choices. Another significant contribution is the release of two new XC datasets that are different from and more challenging than the available benchmark datasets, thereby encouraging more rigorous algorithmic evaluation in the future. Code for LEVER is available at: aka.ms/lever. | https://openreview.net/pdf/a20d3764489727d5956f74f1b28ba95a1d848575.pdf |
Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching | https://openreview.net/forum?id=Ebt7JgMHv1 | https://openreview.net/forum?id=Ebt7JgMHv1 | Aleksandar Makelov,Georg Lange,Atticus Geiger,Neel Nanda | ICLR 2024,Poster | Mechanistic interpretability aims to attribute high-level model behaviors to specific, interpretable learned features. It is hypothesized that these features manifest as directions or low-dimensional subspaces within activation space. Accordingly, recent studies have explored the identification and manipulation of such subspaces to reverse-engineer computations, employing methods such as activation patching. In this work, we demonstrate that naïve approaches to subspace interventions can give rise to interpretability illusions.
Specifically, even if patching along a subspace has the intended end-to-end causal effect on model behavior, this effect may be achieved by activating \emph{a dormant parallel pathway} using a component that is \textit{causally disconnected} from the model output.
We demonstrate this in a mathematical example, realize the example empirically in two different settings (the Indirect Object Identification (IOI) task and factual recall), and argue that activating dormant pathways ought to be prevalent in practice.
In the context of factual recall, we further show that the illusion is related to rank-1 fact editing, providing a mechanistic explanation for previous work observing an inconsistency between fact editing performance and fact localisation.
However, this does not imply that activation patching of subspaces is intrinsically unfit for interpretability.
To contextualize our findings, we also show what a success case looks like in a task (IOI) where prior manual circuit analysis allows an understanding of the location of the ground truth feature. We explore the additional evidence needed to argue that a patched subspace is faithful. | https://openreview.net/pdf/41767a08ea94dbfa362b4807a25ed21035e4dc0e.pdf |
Demonstration-Regularized RL | https://openreview.net/forum?id=lF2aip4Scn | https://openreview.net/forum?id=lF2aip4Scn | Daniil Tiapkin,Denis Belomestny,Daniele Calandriello,Eric Moulines,Alexey Naumov,Pierre Perrault,Michal Valko,Pierre Menard | ICLR 2024,Poster | Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we study the demonstration-regularized reinforcement learning framework that leverages the expert demonstrations by $\mathrm{KL}$-regularization for a policy learned by behavior cloning. Our findings reveal that using $N^{\mathrm{E}}$ expert demonstrations enables the identification of an optimal policy at a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{Poly}(S,A,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in finite and $\widetilde{\mathcal{O}}(\mathrm{Poly}(d,H)/(\varepsilon^2 N^{\mathrm{E}}))$ in linear Markov decision processes, where $\varepsilon$is the target precision, $H$ the horizon, $A$ the number of action, $S$ the number of states in the finite case and $d$ the dimension of the feature space in the linear case. As a by-product, we provide tight convergence guarantees for the behavior cloning procedure under general assumptions on the policy classes. Additionally, we establish that demonstration-regularized methods are provably efficient for reinforcement learning from human feedback (RLHF). In this respect, we provide theoretical evidence showing the benefits of KL-regularization for RLHF in tabular and linear MDPs.
Interestingly, we avoid pessimism injection by employing computationally feasible regularization to handle reward estimation uncertainty, thus setting our approach apart from the prior works. | https://openreview.net/pdf/7f93971543d7b1a26e607380315994e7f7ad3051.pdf |
Multilingual Jailbreak Challenges in Large Language Models | https://openreview.net/forum?id=vESNKdEMGp | https://openreview.net/forum?id=vESNKdEMGp | Yue Deng,Wenxuan Zhang,Sinno Jialin Pan,Lidong Bing | ICLR 2024,Poster | While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to exhibit undesirable behavior. Although several preventive measures have been developed to mitigate the potential risks associated with LLMs, they have primarily focused on English. In this study, we reveal the presence of multilingual jailbreak challenges within LLMs and consider two potential risky scenarios: unintentional and intentional. The unintentional scenario involves users querying LLMs using non-English prompts and inadvertently bypassing the safety mechanisms, while the intentional scenario concerns malicious users combining malicious instructions with multilingual prompts to deliberately attack LLMs. The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases. Specifically, low-resource languages exhibit about three times the likelihood of encountering harmful content compared to high-resource languages, with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts can exacerbate the negative impact of malicious instructions, with astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for GPT-4. To handle such a challenge in the multilingual context, we propose a novel \textsc{Self-Defense} framework that automatically generates multilingual training data for safety fine-tuning. Experimental results show that ChatGPT fine-tuned with such data can achieve a substantial reduction in unsafe content generation. Data is available at \url{https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs}. | https://openreview.net/pdf/5755fd1159bae5a7cf71a490847938b7f047a0b3.pdf |
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence | https://openreview.net/forum?id=RzNlECeoOB | https://openreview.net/forum?id=RzNlECeoOB | Juno Kim,Jaehyuk Kwon,Mincheol Cho,Hyunjong Lee,Joong-Ho Won | ICLR 2024,Poster | The variational autoencoder (VAE) typically employs a standard normal prior as a regularizer for the probabilistic latent encoder. However, the Gaussian tail often decays too quickly to effectively accommodate the encoded points, failing to preserve crucial structures hidden in the data. In this paper, we explore the use of heavy-tailed models to combat over-regularization. Drawing upon insights from information geometry, we propose $t^3$VAE, a modified VAE framework that incorporates Student's t-distributions for the prior, encoder, and decoder. This results in a joint model distribution of a power form which we argue can better fit real-world datasets. We derive a new objective by reformulating the evidence lower bound as joint optimization of KL divergence between two statistical manifolds and replacing with $\gamma$-power divergence, a natural alternative for power families. $t^3$VAE demonstrates superior generation of low-density regions when trained on heavy-tailed synthetic data. Furthermore, we show that $t^3$VAE significantly outperforms other models on CelebA and imbalanced CIFAR-100 datasets. | https://openreview.net/pdf/456160753ce7ca91fd5d14c5ba07b1183322d395.pdf |
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability | https://openreview.net/forum?id=nTwb2vBLOV | https://openreview.net/forum?id=nTwb2vBLOV | Zehao Dong,Muhan Zhang,Philip Payne,Michael A Province,Carlos Cruchaga,Tianyu Zhao,Fuhai Li,Yixin Chen | ICLR 2024,Poster | The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper proposes to maximize the expressivity of GNNs by graph canonization, then the power of such GNNs is studies from the perspective of model stability. A stable GNN will map similar graphs to close graph representations in the vectorial space, and the stability of GNNs is critical to generalize their performance to unseen graphs. We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs. Then we introduce a notion of universal graph canonization as the general solution to address the trade-off and characterize a widely applicable sufficient condition to solve the universal graph canonization. A comprehensive set of experiments demonstrates the effectiveness of the proposed method. In many popular graph benchmark datasets, graph canonization successfully enhances GNNs and provides highly competitive performance, indicating the capability and great potential of proposed method in general graph representation learning. In graph datasets where the sufficient condition holds, GNNs enhanced by universal graph canonization consistently outperform GNN baselines and successfully improve the SOTA performance up to $31$%, providing the optimal solution to numerous challenging real-world graph analytical tasks like gene network representation learning in bioinformatics. | https://openreview.net/pdf/cbad74b981d4d1bb7c06f5c746926121d2dc638f.pdf |
Gradual Optimization Learning for Conformational Energy Minimization | https://openreview.net/forum?id=FMMF1a9ifL | https://openreview.net/forum?id=FMMF1a9ifL | Artem Tsypin,Leonid Anatolievich Ugadiarov,Kuzma Khrabrov,Alexander Telepov,Egor Rumiantsev,Alexey Skrynnik,Aleksandr Panov,Dmitry P. Vetrov,Elena Tutubalina,Artur Kadurin | ICLR 2024,Poster | Molecular conformation optimization is crucial to computer-aided drug discovery and materials design.
Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (oracle) as anti-gradients.
However, this is a computationally expensive approach that requires many interactions with a physical simulator.
One way to accelerate this procedure is to replace the physical simulator with a neural network.
Despite recent progress in neural networks for molecular conformation energy prediction, such models are prone to errors due to distribution shift, leading to inaccurate energy minimization.
We find that the quality of energy minimization with neural networks can be improved by providing optimization trajectories as additional training data.
Still, obtaining complete optimization trajectories demands a lot of additional computations.
To reduce the required additional data, we present the Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks.
The framework consists of an efficient data-collecting scheme and an external optimizer.
The external optimizer utilizes gradients from the energy prediction model to generate optimization trajectories, and the data-collecting scheme selects additional training data to be processed by the physical simulator.
Our results demonstrate that the neural network trained with GOLF performs \textit{on par} with the oracle on a benchmark of diverse drug-like molecules using significantly less additional data. | https://openreview.net/pdf/37b8c4267928b3f02b1f1f1c9df21956eb2c653c.pdf |
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection | https://openreview.net/forum?id=buC4E91xZE | https://openreview.net/forum?id=buC4E91xZE | Qihang Zhou,Guansong Pang,Yu Tian,Shibo He,Jiming Chen | ICLR 2024,Poster | Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary
data to detect anomalies without any training sample in a target dataset. It
is a crucial task when training data is not accessible due to various concerns, e.g.,
data privacy, yet it is challenging since the models need to generalize to anomalies
across different domains where the appearance of foreground objects, abnormal
regions, and background features, such as defects/tumors on different products/
organs, can vary significantly. Recently large pre-trained vision-language
models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition
ability in various vision tasks, including anomaly detection. However, their ZSAD
performance is weak since the VLMs focus more on modeling the class semantics
of the foreground objects rather than the abnormality/normality in the images. In
this paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIP
for accurate ZSAD across different domains. The key insight of AnomalyCLIP
is to learn object-agnostic text prompts that capture generic normality and abnormality
in an image regardless of its foreground objects. This allows our model to
focus on the abnormal image regions rather than the object semantics, enabling
generalized normality and abnormality recognition on diverse types of objects.
Large-scale experiments on 17 real-world anomaly detection datasets show that
AnomalyCLIP achieves superior zero-shot performance of detecting and segmenting
anomalies in datasets of highly diverse class semantics from various defect
inspection and medical imaging domains. Code will be made available at https://github.com/zqhang/AnomalyCLIP. | https://openreview.net/pdf/c8c456c139a7b7f9cbf659ae062d7e3f9cba1aff.pdf |
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery | https://openreview.net/forum?id=uGtfk2OphU | https://openreview.net/forum?id=uGtfk2OphU | Linan Yue,Qi Liu,Yichao Du,Li Wang,Weibo Gao,Yanqing An | ICLR 2024,Poster | The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shortcuts. Then, by introducing the identified shortcuts, we propose two strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods to close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method. | https://openreview.net/pdf/c8c102f26cb7fc4cddf6ff889fe2a63454b563c9.pdf |
CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects | https://openreview.net/forum?id=KTtEICH4TO | https://openreview.net/forum?id=KTtEICH4TO | Yoonyoung Cho,Junhyek Han,Yoontae Cho,Beomjoon Kim | ICLR 2024,Poster | Nonprehensile manipulation is essential for manipulating objects that are too thin, large, or otherwise ungraspable in the wild. To sidestep the difficulty of contact modeling in conventional modeling-based approaches, reinforcement learning (RL) has recently emerged as a promising alternative. However, previous RL approaches either lack the ability to generalize over diverse object shapes, or use simple action primitives that limit the diversity of robot motions. Furthermore, using RL over diverse object geometry is challenging due to the high cost of training a policy that takes in high-dimensional sensory inputs. We propose a novel contact-based object representation and pretraining pipeline to tackle this. To enable massively parallel training, we leverage a lightweight patch-based transformer architecture for our encoder that processes point clouds, thus scaling our training across thousands of environments. Compared to learning from scratch, or other shape representation baselines, our representation facilitates both time- and data-efficient learning. We validate the efficacy of our overall system by zero-shot transferring the trained policy to novel real-world objects. We highly recommend the video attached in the supplementary material. Code and videos are available at \url{https://sites.google.com/view/contact-non-prehensile}. | https://openreview.net/pdf/be6d29e6e7d18c8ea0250289f353011374d395b1.pdf |
An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks | https://openreview.net/forum?id=5JWAOLBxwp | https://openreview.net/forum?id=5JWAOLBxwp | Dongwon Son,Jaehyung Kim,Sanghyeon Son,Beomjoon Kim | ICLR 2024,Poster | The usage of 3D vision algorithms, such as shape reconstruction, remains limited because they require inputs to be at a fixed canonical rotation. Recently, a simple equivariant network, Vector Neuron (VN) has been proposed that can be easily used with the state-of-the-art 3D neural network (NN) architectures. However, its performance is limited because it is designed to use only three-dimensional features, which is insufficient to capture the details present in 3D data. In this paper, we introduce an equivariant feature representation for mapping a 3D point to a high-dimensional feature space. Our feature can discern multiple frequencies present in 3D data, which, as shown by Tancik et al. (2020), is the key to designing an expressive feature for 3D vision tasks. Our representation can be used as an input to VNs, and the results demonstrate that with our feature representation, VN captures more details, overcoming the limitation raised in its original paper. | https://openreview.net/pdf/ae84b2b3de8b202f110cedf0a834de33c23e665d.pdf |
KoLA: Carefully Benchmarking World Knowledge of Large Language Models | https://openreview.net/forum?id=AqN23oqraW | https://openreview.net/forum?id=AqN23oqraW | Jifan Yu,Xiaozhi Wang,Shangqing Tu,Shulin Cao,Daniel Zhang-Li,Xin Lv,Hao Peng,Zijun Yao,Xiaohan Zhang,Hanming Li,Chunyang Li,Zheyuan Zhang,Yushi Bai,Yantao Liu,Amy Xin,Kaifeng Yun,Linlu GONG,Nianyi Lin,Jianhui Chen,Zhili Wu,Yunjia Qi,Weikai Li,Yong Guan,Kaisheng Zeng,Ji Qi,Hailong Jin,Jinxin Liu,Yu Gu,Yuan Yao,Ning Ding,Lei Hou,Zhiyuan Liu,Xu Bin,Jie Tang,Juanzi Li | ICLR 2024,Poster | The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For ability modeling, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering 19 tasks. (2) For data, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For evaluation criteria, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models, and a unique self-contrast metric for automatically evaluating knowledge-creating ability. We evaluate 21 open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset will be updated every three months to provide timely references for developing LLMs and knowledge-related systems. | https://openreview.net/pdf/5b10764ea102a63e929b8b1179abdca8c1903c0a.pdf |
Graph Parsing Networks | https://openreview.net/forum?id=hv3SklibkL | https://openreview.net/forum?id=hv3SklibkL | Yunchong Song,Siyuan Huang,Xinbing Wang,Chenghu Zhou,Zhouhan Lin | ICLR 2024,Poster | Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memory efficiency with preserving node information, depending on whether they use node dropping or node clustering. Additionally, fixed pooling ratios or numbers of pooling layers are predefined for all graphs, which prevents personalized pooling structures from being captured for each individual graph. In this work, inspired by bottom-up grammar induction, we propose an efficient graph parsing algorithm to infer the pooling structure, which then drives graph pooling. The resulting Graph Parsing Network (GPN) adaptively learns personalized pooling structure for each individual graph. GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact. Experimental results on standard benchmarks demonstrate that GPN outperforms state-of-the-art graph pooling methods in graph classification tasks while being able to achieve competitive performance in node classification tasks. We also conduct a graph reconstruction task to show GPN's ability to preserve node information and measure both memory and time efficiency through relevant tests. | https://openreview.net/pdf/d9ce08bd60cb1e4ccd9fa59eade5645174e6c379.pdf |
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts | https://openreview.net/forum?id=N0nTk5BSvO | https://openreview.net/forum?id=N0nTk5BSvO | Hyunwook Lee,Sungahn Ko | ICLR 2024,Poster | Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph. By introducing different experts and properly routing them, TESTAM could better model various circumstances, including spatially isolated nodes, highly related nodes, and recurring and non-recurring events. For the proper routing, we reformulate a gating problem into a classification problem with pseudo labels. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM achieves a better indication and modeling of recurring and non-recurring traffic. | https://openreview.net/pdf/75248a8c97bcbac15b26c02b68788bf7ddb56175.pdf |
Learning From Simplicial Data Based on Random Walks and 1D Convolutions | https://openreview.net/forum?id=OsGUnYOzii | https://openreview.net/forum?id=OsGUnYOzii | Florian Frantzen,Michael T Schaub | ICLR 2024,Poster | Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computational models that operate on higher-order topological domains such as hypergraphs and simplicial complexes. While the increased expressivity of these models can indeed lead to a better classification performance and a more faithful representation of the underlying system, the computational cost of these higher-order models can increase dramatically. To this end, we here explore a simplicial complex neural network learning architecture based on random walks and fast 1D convolutions (SCRaWl), in which we can adjust the increase in computational cost by varying the length and number of random walks considered while accounting for higher-order relationships. Importantly, due to the random walk-based design, the expressivity of the proposed architecture is provably incomparable to that of existing message-passing simplicial neural networks. We empirically evaluate SCRaWl on real-world datasets and show that it outperforms other simplicial neural networks. | https://openreview.net/pdf/f7f8f6134614bab4c34752266fafd849070821c0.pdf |
LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition | https://openreview.net/forum?id=wkbeqr5XhC | https://openreview.net/forum?id=wkbeqr5XhC | Lingfeng Liu,Dong Ni,Hangjie Yuan | ICLR 2024,Poster | Bandwidth constraints during signal acquisition frequently impede real-time detection applications. Hyperspectral data is a notable example, whose vast volume compromises real-time hyperspectral detection. To tackle this hurdle, we introduce a novel approach leveraging pre-acquisition modulation to reduce the acquisition volume. This modulation process is governed by a deep learning model, utilizing prior information. Central to our approach is LUM-ViT, a Vision Transformer variant. Uniquely, LUM-ViT incorporates a learnable under-sampling mask tailored for pre-acquisition modulation. To further optimize for optical calculations, we propose a kernel-level weight binarization technique and a three-stage fine-tuning strategy. Our evaluations reveal that, by sampling a mere 10\% of the original image pixels, LUM-ViT maintains the accuracy loss within 1.8\% on the ImageNet classification task. The method sustains near-original accuracy when implemented on real-world optical hardware, demonstrating its practicality. Code will be available at [https://github.com/MaxLLF/LUM-ViT](https://github.com/MaxLLF/LUM-ViT). | https://openreview.net/pdf/e727a272852fd9b151a8c6fee5946d926aa8f97c.pdf |
Social-Transmotion: Promptable Human Trajectory Prediction | https://openreview.net/forum?id=SQpnEfv9WH | https://openreview.net/forum?id=SQpnEfv9WH | Saeed Saadatnejad,Yang Gao,Kaouther Messaoud,Alexandre Alahi | ICLR 2024,Poster | Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating the space.
To address this, we introduce *Social-Transmotion*, a generic Transformer-based model that exploits diverse and numerous visual cues to predict human behavior. We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes in the image plane, or body pose keypoints in either 2D or 3D. This, in turn, augments trajectory data, leading to enhanced human trajectory prediction.
Using masking technique, our model exhibits flexibility and adaptability by capturing spatiotemporal interactions between agents based on the available visual cues.
We delve into the merits of using 2D versus 3D poses, and a limited set of poses. Additionally, we investigate the spatial and temporal attention map to identify which keypoints and time-steps in the sequence are vital for optimizing human trajectory prediction.
Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY.
The code is publicly available: [https://github.com/vita-epfl/social-transmotion](https://github.com/vita-epfl/social-transmotion). | https://openreview.net/pdf/5dcf47a66eb9a88f6cda4a59dfb2dd17df12bc68.pdf |
Robust Classification via Regression for Learning with Noisy Labels | https://openreview.net/forum?id=wfgZc3IMqo | https://openreview.net/forum?id=wfgZc3IMqo | Erik Englesson,Hossein Azizpour | ICLR 2024,Poster | Deep neural networks and large-scale datasets have revolutionized the field of machine learning. However, these large networks are susceptible to overfitting to label noise, resulting in reduced generalization. To address this challenge, two promising approaches have emerged: i) loss reweighting, which reduces the influence of noisy examples on the training loss, and ii) label correction that replaces noisy labels with estimated true labels. These directions have been pursued separately or combined as independent methods, lacking a unified approach. In this work, we present a unified method that seamlessly combines loss reweighting and label correction to enhance robustness against label noise in classification tasks. Specifically, by leveraging ideas from compositional data analysis in statistics, we frame the problem as a regression task, where loss reweighting and label correction can naturally be achieved with a shifted Gaussian label noise model. Our unified approach achieves strong performance compared to recent baselines on several noisy labelled datasets. We believe this work is a promising step towards robust deep learning in the presence of label noise. Our code is available at: https://github.com/ErikEnglesson/SGN. | https://openreview.net/pdf/dc6d5771bf99d6ea8d806a95a0283ab492a6448c.pdf |
Learning to Reject with a Fixed Predictor: Application to Decontextualization | https://openreview.net/forum?id=dCHbFDsCZz | https://openreview.net/forum?id=dCHbFDsCZz | Christopher Mohri,Daniel Andor,Eunsol Choi,Michael Collins,Anqi Mao,Yutao Zhong | ICLR 2024,Poster | We study the problem of classification with a reject option for a fixed predictor, crucial to natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We provide a complete theoretical analysis of the surrogate loss function with a strong $H$-consistency guarantee. For evaluation, we choose the \textit{decontextualization} task, and provide a manually-labelled dataset of $2\mathord,000$ examples. Our algorithm significantly outperforms the baselines considered, with a $\sim 25$% improvement in coverage when halving the error rate, which is only $\sim 3$% away from the theoretical limit. | https://openreview.net/pdf/66caaecc6922bf5ef4e221d3e575e0c06e70075a.pdf |
Dynamics-Informed Protein Design with Structure Conditioning | https://openreview.net/forum?id=jZPqf2G9Sw | https://openreview.net/forum?id=jZPqf2G9Sw | Urszula Julia Komorowska,Simon V Mathis,Kieran Didi,Francisco Vargas,Pietro Lio,Mateja Jamnik | ICLR 2024,Poster | Current protein generative models are able to design novel backbones with desired shapes or functional motifs. However, despite the importance of a protein’s dynamical properties for its function, conditioning on dynamical properties remains elusive. We present a new approach to protein generative modeling by leveraging Normal Mode Analysis that enables us to capture dynamical properties too. We introduce a method for conditioning the diffusion probabilistic models on protein dynamics, specifically on the lowest non-trivial normal mode of oscillation. Our method, similar to the classifier guidance conditioning, formulates the sampling process as being driven by conditional and unconditional terms. However, unlike previous works, we approximate the conditional term with a simple analytical function rather than an external neural network, thus making the eigenvector calculations approachable. We present the corresponding SDE theory as a formal justification of our approach. We extend our framework to conditioning on structure and dynamics at the same time, enabling scaffolding of the dynamical motifs. We demonstrate the empirical effectiveness of our method by turning the open-source unconditional protein diffusion model Genie into the conditional model with no retraining. Generated proteins exhibit the desired dynamical and structural properties while still being biologically plausible. Our work represents a first step towards incorporating dynamical behaviour in protein design and may open the door to designing more flexible and functional proteins in the future. | https://openreview.net/pdf/0ffbbb38174d8802162c0bef4914451f67318f38.pdf |
Partitioning Message Passing for Graph Fraud Detection | https://openreview.net/forum?id=tEgrUrUuwA | https://openreview.net/forum?id=tEgrUrUuwA | Wei Zhuo,Zemin Liu,Bryan Hooi,Bingsheng He,Guang Tan,Rizal Fathony,Jia Chen | ICLR 2024,Poster | Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily, by excluding heterophilic neighbors during message passing. In our work, we argue that the key to applying GNNs for GFD is not to exclude but to {\em distinguish} neighbors with different labels. Grounded in this perspective, we introduce Partitioning Message Passing (PMP), an intuitive yet effective message passing paradigm expressly crafted for GFD. Specifically, in the neighbor aggregation stage of PMP, neighbors with different classes are aggregated with distinct node-specific aggregation functions. By this means, the center node can adaptively adjust the information aggregated from its heterophilic and homophilic neighbors, thus avoiding the model gradient being dominated by benign nodes which occupy the majority of the population. We theoretically establish a connection between the spatial formulation of PMP and spectral analysis to characterize that PMP operates an adaptive node-specific spectral graph filter, which demonstrates the capability of PMP to handle heterophily-homophily mixed graphs. Extensive experimental results show that PMP can significantly boost the performance on GFD tasks. | https://openreview.net/pdf/7abe6053f04ef235ce8ddd5996dd27e266c6e058.pdf |
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation | https://openreview.net/forum?id=EmQSOi1X2f | https://openreview.net/forum?id=EmQSOi1X2f | Niels Mündler,Jingxuan He,Slobodan Jenko,Martin Vechev | ICLR 2024,Poster | Large language models (large LMs) are susceptible to producing text that contains hallucinated content. An important instance of this problem is self-contradiction, where the LM generates two contradictory sentences within the same context. In this work, we present a comprehensive investigation into self-contradiction for various instruction-tuned LMs, covering evaluation, detection, and mitigation. Our primary evaluation task is open-domain text generation, but we also demonstrate the applicability of our approach to shorter question answering. Our analysis reveals the prevalence of self-contradictions, e.g., in 17.7% of all sentences produced by ChatGPT. We then propose a novel prompting-based framework designed to effectively detect and mitigate self-contradictions. Our detector achieves high accuracy, e.g., around 80% F1 score when prompting ChatGPT. The mitigation algorithm iteratively refines the generated text to remove contradictory information while preserving text fluency and informativeness. Importantly, our entire framework is applicable to black-box LMs and does not require retrieval of external knowledge. Rather, our method complements retrieval-based methods, as a large portion of self-contradictions (e.g., 35.2% for ChatGPT) cannot be verified using online text. Our approach is practically effective and has been released as a push-button tool to benefit the public at https://chatprotect.ai/. | https://openreview.net/pdf/9087c7d18bb7707f5f8b47046499f922b5c47af3.pdf |
Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching | https://openreview.net/forum?id=AyXIDfvYg8 | https://openreview.net/forum?id=AyXIDfvYg8 | Ganesh Ramachandra Kini,Vala Vakilian,Tina Behnia,Jaidev Gill,Christos Thrampoulidis | ICLR 2024,Poster | Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification. While prior studies have demonstrated that both losses yield symmetric training representations under balanced data, this symmetry breaks under class imbalances. This paper presents an intriguing discovery: the introduction of a ReLU activation at the final layer effectively restores the symmetry in SCL-learned representations. We arrive at this finding analytically, by establishing that the global minimizers of an unconstrained features model with SCL loss and entry-wise non-negativity constraints form an orthogonal frame. Extensive experiments conducted across various datasets, architectures, and imbalance scenarios corroborate our finding. Importantly, our experiments reveal that the inclusion of the ReLU activation restores symmetry without compromising test accuracy. This constitutes the first geometry characterization of SCL under imbalances. Additionally, our analysis and experiments underscore the pivotal role of batch selection strategies in representation geometry. By proving necessary and sufficient conditions for mini-batch choices that ensure invariant symmetric representations, we introduce batch-binding as an efficient strategy that guarantees these conditions hold. | https://openreview.net/pdf/b40ab759531e71a4137172d4dca6019ea210d8d1.pdf |
Manipulating dropout reveals an optimal balance of efficiency and robustness in biological and machine visual systems | https://openreview.net/forum?id=ADDCErFzev | https://openreview.net/forum?id=ADDCErFzev | Jacob S. Prince,Gabriel Fajardo,George A. Alvarez,Talia Konkle | ICLR 2024,Poster | According to the efficient coding hypothesis, neural populations encode information optimally when representations are high-dimensional and uncorrelated. However, such codes may carry a cost in terms of generalization and robustness. Past empirical studies of early visual cortex (V1) in rodents have suggested that this tradeoff indeed constrains sensory representations. However, it remains unclear whether these insights generalize across the hierarchy of the human visual system, and particularly to object representations in high-level occipitotemporal cortex (OTC). To gain new empirical clarity, here we develop a family of object recognition models with parametrically varying dropout proportion $p$, which induces systematically varying dimensionality of internal responses (while controlling all other inductive biases). We find that increasing dropout produces an increasingly smooth, low-dimensional representational space. Optimal robustness to lesioning is observed at around 70% dropout, after which both accuracy and robustness decline. Representational comparison to large-scale 7T fMRI data from occipitotemporal cortex in the Natural Scenes Dataset reveals that this optimal degree of dropout is also associated with maximal emergent neural predictivity. Finally, using new techniques for achieving denoised estimates of the eigenspectrum of human fMRI responses, we compare the rate of eigenspectrum decay between model and brain feature spaces. We observe that the match between model and brain representations is associated with a common balance between efficiency and robustness in the representational space. These results suggest that varying dropout may reveal an optimal point of balance between the efficiency of high-dimensional codes and the robustness of low dimensional codes in hierarchical vision systems. | https://openreview.net/pdf/7f10cc682acaa93ecdd9fa4c23e343be124115dd.pdf |
DDMI: Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations | https://openreview.net/forum?id=327tbF3S65 | https://openreview.net/forum?id=327tbF3S65 | Dogyun Park,Sihyeon Kim,Sojin Lee,Hyunwoo J. Kim | ICLR 2024,Poster | Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains. These models opened the door for domain-agnostic generative models, but they often fail to achieve high-quality generation. We observed that the existing methods generate the weights of neural networks to parameterize INRs and evaluate the network with fixed positional embeddings (PEs). Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation. To address this limitation, we propose Domain-agnostic Latent Diffusion Model for INRs (DDMI) that generates adaptive positional embeddings instead of neural networks' weights. Specifically, we develop a Discrete-to-continuous space Variational AutoEncoder (D2C-VAE) that seamlessly connects discrete data and continuous signal functions in the shared latent space. Additionally, we introduce a novel conditioning mechanism for evaluating INRs with the hierarchically decomposed PEs to further enhance expressive power. Extensive experiments across four modalities, \eg, 2D images, 3D shapes, Neural Radiance Fields, and videos, with seven benchmark datasets, demonstrate the versatility of DDMI and its superior performance compared to the existing INR generative models. Code is available at \href{https://github.com/mlvlab/DDMI}{https://github.com/mlvlab/DDMI}. | https://openreview.net/pdf/716a67cdede3543daa6309c204a066e517decc41.pdf |
Bayesian Coreset Optimization for Personalized Federated Learning | https://openreview.net/forum?id=uz7d2N2zul | https://openreview.net/forum?id=uz7d2N2zul | Prateek Chanda,Shrey Modi,Ganesh Ramakrishnan | ICLR 2024,Poster | In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbersome. To address this issue we propose CORESET-PFEDBAYES : a personalized coreset weighted federated learning setup where the training updates for each individual clients are forwarded to the central server based on only individual client coreset based representative data points instead of the entire client data. Through theoretical analysis we present how the average generalization error is minimax optimal up to logarithm bounds (upper bounded by $\mathcal{O}(n_k^{-\frac{2 \beta}{2 \beta+\boldsymbol{\Lambda}}} \log ^{2 \delta^{\prime}}(n_k))$) and lower bounds of $\mathcal{O}(n_k^{-\frac{2 \beta}{2 \beta+\boldsymbol{\Lambda}}})$, and how the overall generalization error on the data likelihood differs from a vanilla Federated Learning setup as a closed form function ${\boldsymbol{\Im}}(\boldsymbol{w}, n_k)$ of the coreset weights $\boldsymbol{w}$ and coreset sample size $n_k$.
Our experiments on different benchmark datasets based on a variety of recent personalized federated learning architectures show significant gains as compared to random sampling on the training data followed by federated learning, thereby indicating how intelligently selecting such training samples can help in performance. Additionally, through experiments on medical datasets our proposed method showcases some gains as compared to other submodular optimization based approaches used for subset selection on client's data. | https://openreview.net/pdf/cd650d7bda1c2da0c78084f2b993c15c2ec0925f.pdf |
In-context Autoencoder for Context Compression in a Large Language Model | https://openreview.net/forum?id=uREj4ZuGJE | https://openreview.net/forum?id=uREj4ZuGJE | Tao Ge,Hu Jing,Lei Wang,Xun Wang,Si-Qing Chen,Furu Wei | ICLR 2024,Poster | We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes. ICAE is first pretrained using both autoencoding and language modeling objectives on massive text data, enabling it to generate memory slots that accurately and comprehensively represent the original context. Then, it is fine-tuned on instruction data for producing desirable responses to various prompts. Experiments demonstrate that our lightweight ICAE, introducing about 1% additional parameters, effectively achieves $4\times$ context compression based on Llama, offering advantages in both improved latency and GPU memory cost during inference, and showing an interesting insight in memorization as well as potential for scalability. These promising results imply a novel perspective on the connection between working memory in cognitive science and representation learning in LLMs, revealing ICAE's significant implications in addressing the long context problem and suggesting further research in LLM context management. Our data, code and models are available at https://github.com/getao/icae. | https://openreview.net/pdf/0cb80bedd6a43e1383e8fe4642b39105d27be261.pdf |
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning | https://openreview.net/forum?id=J44HfH4JCg | https://openreview.net/forum?id=J44HfH4JCg | Fuxiao Liu,Kevin Lin,Linjie Li,Jianfeng Wang,Yaser Yacoob,Lijuan Wang | ICLR 2024,Poster | Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset comprises 400k visual
instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at three semantic levels: (i) Nonexistent Object Manipulation, (ii) Existent Object Manipulation and (iii) Knowledge Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts. GAVIE does not require human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate existing LMMs exhibit significant hallucinations when presented with our negative instructions, particularly Existent Object and Knowledge Manipulation instructions. Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public
datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model. Code and data will be released upon publication. | https://openreview.net/pdf/a7dc45dbafdb5c8be70b9ae7f987f44a08b62885.pdf |
Multimarginal Generative Modeling with Stochastic Interpolants | https://openreview.net/forum?id=FHqAzWl2wE | https://openreview.net/forum?id=FHqAzWl2wE | Michael Samuel Albergo,Nicholas Matthew Boffi,Michael Lindsey,Eric Vanden-Eijnden | ICLR 2024,Poster | Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The structure of this joint distribution should identify multi-way correspondences among the prescribed marginals. We formalize an approach to this task within a generalization of the stochastic interpolant framework, leading to efficient learning algorithms built upon dynamical transport of measure. Our generative models are defined by velocity and score fields that can be characterized as the minimizers of simple quadratic objectives, and they are defined on a simplex that generalizes the time variable in the usual dynamical transport framework. The resulting transport on the simplex is influenced by all marginals, and we show that multi-way correspondences can be extracted. The identification of such correspondences has applications to style transfer, algorithmic fairness, and data decorruption. In addition, the multimarginal perspective enables an efficient algorithm for optimizing the dynamical transport cost in the ordinary two-marginal setting. We demonstrate these capacities with several numerical examples. | https://openreview.net/pdf/ffbe0129551a2886a81d49db461b05400f637dee.pdf |
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators | https://openreview.net/forum?id=JiTVtCUOpS | https://openreview.net/forum?id=JiTVtCUOpS | Lifan Zhao,Yanyan Shen | ICLR 2024,Poster | Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods miss potential opportunities in utilizing channel dependence for accurate predictions. We argue that there exist locally stationary lead-lag relationships between variates, i.e., some lagged variates may follow the leading indicators within a short time period. Exploiting such channel dependence is beneficial since leading indicators offer advance information that can be used to reduce the forecasting difficulty of the lagged variates. In this paper, we propose a new method named LIFT that first efficiently estimates leading indicators and their leading steps at each time step and then judiciously allows the lagged variates to utilize the advance information from leading indicators. LIFT plays as a plugin that can be seamlessly collaborated with arbitrary time series forecasting methods. Extensive experiments on six real-world datasets demonstrate that LIFT improves the state-of-the-art methods by 5.4% in average forecasting performance. Our code is available at https://github.com/SJTU-Quant/LIFT. | https://openreview.net/pdf/d434e3f2213d54e8999f551b44bbc2454529e8fb.pdf |
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