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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis | https://openreview.net/forum?id=di52zR8xgf | https://openreview.net/forum?id=di52zR8xgf | Dustin Podell,Zion English,Kyle Lacey,Andreas Blattmann,Tim Dockhorn,Jonas Müller,Joe Penna,Robin Rombach | ICLR 2024,Spotlight | We present Stable Diffusion XL (SDXL), a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone, achieved by significantly increasing the number of attention blocks and including a second text encoder. Further, we design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. To ensure highest quality results, we also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL improves dramatically over previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators such as Midjourney. | https://openreview.net/pdf/13e7093b32d96bdf98f6af87ef27a4f89585f067.pdf |
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels | https://openreview.net/forum?id=uDxeSZ1wdI | https://openreview.net/forum?id=uDxeSZ1wdI | Dan Haramati,Tal Daniel,Aviv Tamar | ICLR 2024,Spotlight | Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from raw image observations. In this work we propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction, and use it to learn goal-conditioned manipulation of several objects. Key to our method is the ability to handle goals with dependencies between the objects (e.g., moving objects in a certain order). We further relate our architecture to the generalization capability of the trained agent, based on a theoretical result for compositional generalization, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Videos and code are available on the project website: https://sites.google.com/view/entity-centric-rl | https://openreview.net/pdf/2627c85192603f1981d419b5964256fa755a98c8.pdf |
Constrained Bi-Level Optimization: Proximal Lagrangian Value Function Approach and Hessian-free Algorithm | https://openreview.net/forum?id=xJ5N8qrEPl | https://openreview.net/forum?id=xJ5N8qrEPl | Wei Yao,Chengming Yu,Shangzhi Zeng,Jin Zhang | ICLR 2024,Spotlight | This paper presents a new approach and algorithm for solving a class of constrained Bi-Level Optimization (BLO) problems in which the lower-level problem involves constraints coupling both upper-level and lower-level variables. Such problems have recently gained significant attention due to their broad applicability in machine learning. However, conventional gradient-based methods unavoidably rely on computationally intensive calculations related to the Hessian matrix. To address this challenge, we devise a smooth proximal Lagrangian value function to handle the constrained lower-level problem. Utilizing this construct, we introduce a single-level reformulation for constrained BLOs that transforms the original BLO problem into an equivalent optimization problem with smooth constraints. Enabled by this reformulation, we develop a Hessian-free gradient-based algorithm—termed proximal Lagrangian Value function-based Hessian-free Bi-level Algorithm (LV-HBA)—that is straightforward to implement in a single loop manner. Consequently, LV-HBA is especially well-suited for machine learning applications. Furthermore, we offer non-asymptotic convergence analysis for LV-HBA, eliminating the need for traditional strong convexity assumptions for the lower-level problem while also being capable of accommodating non-singleton scenarios. Empirical results substantiate the algorithm's superior practical performance. | https://openreview.net/pdf/83a8490d92a68039f334e84d5eef6e970f4315e5.pdf |
Inherently Interpretable Time Series Classification via Multiple Instance Learning | https://openreview.net/forum?id=xriGRsoAza | https://openreview.net/forum?id=xriGRsoAza | Joseph Early,Gavin Cheung,Kurt Cutajar,Hanting Xie,Jas Kandola,Niall Twomey | ICLR 2024,Spotlight | Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and propose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learning TSC models and show how they become inherently interpretable without compromising (and in some cases, even improving) predictive performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel synthetic dataset that is specially designed to facilitate interpretability evaluation. On these datasets, we show MILLET produces sparse explanations quickly that are of higher quality than other well-known interpretability methods. To the best of our knowledge, our work with MILLET is the first to develop general MIL methods for TSC and apply them to an extensive variety of domains. | https://openreview.net/pdf/b63fbc2c94da98976eead6f787efe4df88f0e8a7.pdf |
A Mutual Information Perspective on Federated Contrastive Learning | https://openreview.net/forum?id=JrmPG9ufKg | https://openreview.net/forum?id=JrmPG9ufKg | Christos Louizos,Matthias Reisser,Denis Korzhenkov | ICLR 2024,Spotlight | We investigate contrastive learning in the federated setting through the lens of Sim- CLR and multi-view mutual information maximization. In doing so, we uncover a connection between contrastive representation learning and user verification; by adding a user verification loss to each client’s local SimCLR loss we recover a lower bound to the global multi-view mutual information. To accommodate for the case of when some labelled data are available at the clients, we extend our SimCLR variant to the federated semi-supervised setting. We see that a supervised SimCLR objective can be obtained with two changes: a) the contrastive loss is computed between datapoints that share the same label and b) we require an additional auxiliary head that predicts the correct labels from either of the two views. Along with the proposed SimCLR extensions, we also study how different sources of non-i.i.d.-ness can impact the performance of federated unsupervised learning through global mutual information maximization; we find that a global objective is beneficial for some sources of non-i.i.d.-ness but can be detrimental for others. We empirically evaluate our proposed extensions in various tasks to validate our claims and furthermore demonstrate that our proposed modifications generalize to other pretraining methods. | https://openreview.net/pdf/5dc1a3c5ed8b71611436f05451243c4bea50fd66.pdf |
MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy | https://openreview.net/forum?id=GZ6AcZwA8r | https://openreview.net/forum?id=GZ6AcZwA8r | Yan Sun,Jicong Fan | ICLR 2024,Spotlight | This paper focuses on graph metric learning. First, we present a class of maximum mean discrepancy (MMD) based graph kernels, called MMD-GK. These kernels are computed by applying MMD to the node representations of two graphs with message-passing propagation.
Secondly, we provide a class of deep MMD-GKs that are able to learn graph kernels and implicit graph features adaptively in an unsupervised manner. Thirdly, we propose a class of supervised deep MMD-GKs that are able to utilize label information of graphs and hence yield more discriminative metrics. Besides the algorithms, we provide theoretical analysis for the proposed methods. The proposed methods are evaluated in comparison to many baselines such as graph kernels and graph neural networks in the tasks of graph clustering and graph classification. The numerical results demonstrate the effectiveness and superiority of our methods. | https://openreview.net/pdf/618ed39d01f843e5ce4cab3fd3086ea9061d926b.pdf |
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem | https://openreview.net/forum?id=HgOJlxzB16 | https://openreview.net/forum?id=HgOJlxzB16 | Margalit Glasgow | ICLR 2024,Spotlight | In this work, we consider the optimization process of minibatch stochastic gradient descent (SGD) on a 2-layer neural network with data separated by a quadratic ground truth function. We prove that with data drawn from the Boolean hypercube labeled by the quadratic ``XOR'' function $y = -x_ix_j$ , it is possible to train to a population error $o(1)$
with $\Theta(d\text{polylog}(d))$ samples. Our result considers simultaneously training both layers of the two-layer-neural network with ReLU activations via standard minibatch SGD on the logistic loss. To our knowledge, this work is the first to give a sample complexity of
for efficiently learning the XOR function on isotropic data on a standard neural network with standard training. Our main technique is showing that the network evolves in two phases: a \em signal-finding \em phase where the network is small and many of the neurons evolve independently to find features, and a \em signal-heavy \em phase, where SGD maintains and balances the features. We leverage the simultaneous training of the layers to show that it is sufficient for only a small fraction of the neurons to learn features, since those neurons will be amplified by the simultaneous growth of their second layer weights. | https://openreview.net/pdf/d00625c7d5210386b43eec8c623aa8b9a3158712.pdf |
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks | https://openreview.net/forum?id=gjfOL9z5Xr | https://openreview.net/forum?id=gjfOL9z5Xr | Kaijie Zhu,Jiaao Chen,Jindong Wang,Neil Zhenqiang Gong,Diyi Yang,Xing Xie | ICLR 2024,Spotlight | Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs.
In this paper, we introduce DyVal, a general and flexible protocol for dynamic evaluation of LLMs. Based on our framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to GPT-3.5-Turbo and GPT-4. Experiments show that LLMs perform worse in DyVal-generated evaluation samples with different complexities, highlighting the significance of dynamic evaluation.
We also analyze the failure cases and results of different prompting methods.
Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks.
We hope that DyVal can shed light on future evaluation research of LLMs. Code is available at: https://github.com/microsoft/promptbench. | https://openreview.net/pdf/997160014cb22fbfafa276c6b83c6cb0ebc92e9f.pdf |
Illusory Attacks: Information-theoretic detectability matters in adversarial attacks | https://openreview.net/forum?id=F5dhGCdyYh | https://openreview.net/forum?id=F5dhGCdyYh | Tim Franzmeyer,Stephen Marcus McAleer,Joao F. Henriques,Jakob Nicolaus Foerster,Philip Torr,Adel Bibi,Christian Schroeder de Witt | ICLR 2024,Spotlight | Autonomous agents deployed in the real world need to be robust against adversarial attacks on sensory inputs.
Robustifying agent policies requires anticipating the strongest attacks possible.
We demonstrate that existing observation-space attacks on reinforcement learning agents have a common weakness: while effective, their lack of information-theoretic detectability constraints makes them \textit{detectable} using automated means or human inspection.
Detectability is undesirable to adversaries as it may trigger security escalations.
We introduce \textit{\eattacks{}}, a novel form of adversarial attack on sequential decision-makers that is both effective and of $\epsilon-$bounded statistical detectability.
We propose a novel dual ascent algorithm to learn such attacks end-to-end.
Compared to existing attacks, we empirically find \eattacks{} to be significantly harder to detect with automated methods, and a small study with human participants\footnote{IRB approval under reference R84123/RE001} suggests they are similarly harder to detect for humans.
Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses. The project website can be found at https://tinyurl.com/illusory-attacks. | https://openreview.net/pdf/d66e1f533261c55834c022529b10774843d7e452.pdf |
Addressing Signal Delay in Deep Reinforcement Learning | https://openreview.net/forum?id=Z8UfDs4J46 | https://openreview.net/forum?id=Z8UfDs4J46 | Wei Wang,Dongqi Han,Xufang Luo,Dongsheng Li | ICLR 2024,Spotlight | Despite the notable advancements in deep reinforcement learning (DRL) in recent years, a prevalent issue that is often overlooked is the impact of signal delay. Signal delay occurs when there is a lag between an agent's perception of the environment and its corresponding actions. In this paper, we first formalize delayed-observation Markov decision processes (DOMDP) by extending the standard MDP framework to incorporate signal delays. Next, we elucidate the challenges posed by the presence of signal delay in DRL, showing that trivial DRL algorithms and generic methods for partially observable tasks suffer greatly from delays. Lastly, we propose effective strategies to overcome these challenges. Our methods achieve remarkable performance in continuous robotic control tasks with large delays, yielding results comparable to those in non-delayed cases. Overall, our work contributes to a deeper understanding of DRL in the presence of signal delays and introduces novel approaches to address the associated challenges. | https://openreview.net/pdf/56d8f487fd9b7b18c6c5bbea5d5d311be7d0eb5e.pdf |
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis | https://openreview.net/forum?id=qTlcbLSm4p | https://openreview.net/forum?id=qTlcbLSm4p | Jiayan Teng,Wendi Zheng,Ming Ding,Wenyi Hong,Jianqiao Wangni,Zhuoyi Yang,Jie Tang | ICLR 2024,Spotlight | Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the main reason is that *the same noise level on a higher resolution results in a higher Signal-to-Noise Ratio in the frequency domain*. In this work, we present Relay Diffusion Model (RDM), which transfers a low-resolution image or noise into an equivalent high-resolution one for diffusion model via blurring diffusion and block noise. Therefore, the diffusion process can continue seamlessly in any new resolution or model without restarting from pure noise or low-resolution conditioning. RDM achieves state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256, surpassing previous works such as ADM, LDM and DiT by a large margin. All the codes and checkpoints are open-sourced at \url{https://github.com/THUDM/RelayDiffusion}. | https://openreview.net/pdf/6c6bde8db08956621d943921475d8db656889420.pdf |
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models | https://openreview.net/forum?id=u48tHG5f66 | https://openreview.net/forum?id=u48tHG5f66 | Yingqing He,Shaoshu Yang,Haoxin Chen,Xiaodong Cun,Menghan Xia,Yong Zhang,Xintao Wang,Ran He,Qifeng Chen,Ying Shan | ICLR 2024,Spotlight | In this work, we investigate the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes. In addition, the generated images should have arbitrary image aspect ratios. When generating images directly at a higher resolution, 1024 x 1024, with the pre-trained Stable Diffusion using training images of resolution 512 x 512, we observe persistent problems of object repetition and unreasonable object structures. Existing works for higher-resolution generation, such as attention-based and joint-diffusion approaches, cannot well address these issues. As a new perspective, we examine the structural components of the U-Net in diffusion models and identify the crucial cause as the limited perception field of convolutional kernels. Based on this key observation, we propose a simple yet effective re-dilation that can dynamically adjust the convolutional perception field during inference. We further propose the dispersed convolution and noise-damped classifier-free guidance, which can enable ultra-high-resolution image generation (e.g., 4096 x 4096). Notably, our approach does not require any training or optimization. Extensive experiments demonstrate that our approach can address the repetition issue well and achieve state-of-the-art performance on higher-resolution image synthesis, especially in texture details. Our work also suggests that a pre-trained diffusion model trained on low-resolution images can be directly used for high-resolution visual generation without further tuning, which may provide insights for future research on ultra-high-resolution image and video synthesis. More results are available at the anonymous website: https://scalecrafter.github.io/ScaleCrafter/ | https://openreview.net/pdf/b3127432d984fcf8fdd23acb6f94df6a0f0d8752.pdf |
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization | https://openreview.net/forum?id=MSe8YFbhUE | https://openreview.net/forum?id=MSe8YFbhUE | Guowei Xu,Ruijie Zheng,Yongyuan Liang,Xiyao Wang,Zhecheng Yuan,Tianying Ji,Yu Luo,Xiaoyu Liu,Jiaxin Yuan,Pu Hua,Shuzhen Li,Yanjie Ze,Hal Daumé III,Furong Huang,Huazhe Xu | ICLR 2024,Spotlight | Visual reinforcement learning (RL) has shown promise in continuous control tasks.
Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds.
In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively.
Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks.
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals.
Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio.
Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit.
Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations. | https://openreview.net/pdf/1c921e84904e3fe06d5ed433201fa0fb2d619046.pdf |
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization | https://openreview.net/forum?id=xGvPKAiOhq | https://openreview.net/forum?id=xGvPKAiOhq | Nuoya Xiong,Lijun Ding,Simon Shaolei Du | ICLR 2024,Spotlight | This paper rigorously shows how over-parameterization dramatically changes the convergence behaviors of gradient descent (GD) for the matrix sensing problem, where the goal is to recover an unknown low-rank ground-truth matrix from near-isotropic linear measurements.
First, we consider the symmetric setting with the symmetric parameterization where $M^* \in \mathbb{R}^{n \times n}$ is a positive semi-definite unknown matrix of rank $r \ll n$, and one uses a symmetric parameterization $XX^\top$ to learn $M^*$. Here $X \in \mathbb{R}^{n \times k}$ with $k > r$ is the factor matrix. We give a novel $\Omega\left(1/T^2\right)$ lower bound of randomly initialized GD for the over-parameterized case ($k >r$) where $T$ is the number of iterations. This is in stark contrast to the exact-parameterization scenario ($k=r$) where the convergence rate is $\exp\left(-\Omega\left(T\right)\right)$. Next, we study asymmetric setting where $M^* \in \mathbb{R}^{n_1 \times n_2}$ is the unknown matrix of rank $r \ll \min\{n_1,n_2\}$, and one uses an asymmetric parameterization $FG^\top$ to learn $M^*$ where $F \in \mathbb{R}^{n_1 \times k}$ and $G \in \mathbb{R}^{n_2 \times k}$. We give the first global exact convergence result of randomly initialized GD for the exact-parameterization case ($k=r$) with an $\exp\left(-\Omega\left(T\right)\right)$ rate. Furthermore, we give the first global exact convergence result for the over-parameterization case ($k>r$) with an $\exp\left(-\Omega\left(\alpha^2 T\right)\right)$ rate where $\alpha$ is the initialization scale. This linear convergence result in the over-parameterization case is especially significant because one can apply the asymmetric parameterization to the symmetric setting to speed up from $\Omega\left(1/T^2\right)$ to linear convergence. Therefore, we identify a surprising phenomenon: asymmetric parameterization can exponentially speed up convergence. Equally surprising is our analysis that highlights the importance of imbalance between $F$ and $G$. This is in sharp contrast to prior works which emphasize balance. We further give an example showing the dependency on $\alpha$ in the convergence rate is unavoidable in the worst case. On the other hand, we propose a novel method that only modifies one step of GD and obtains a convergence rate independent of $\alpha$, recovering the rate in the exact-parameterization case. We provide empirical studies to verify our theoretical findings. | https://openreview.net/pdf/85f424e5914d2f97c9b19f6dc6f35512876e93ac.pdf |
AnyText: Multilingual Visual Text Generation and Editing | https://openreview.net/forum?id=ezBH9WE9s2 | https://openreview.net/forum?id=ezBH9WE9s2 | Yuxiang Tuo,Wangmeng Xiang,Jun-Yan He,Yifeng Geng,Xuansong Xie | ICLR 2024,Spotlight | Diffusion model based Text-to-Image has achieved impressive achievements recently. Although current technology for synthesizing images is highly advanced and capable of generating images with high fidelity, it is still possible to give the show away when focusing on the text area in the generated image, as synthesized text often contains blurred, unreadable, or incorrect characters, making visual text generation one of the most challenging issues in this field. To address this issue, we introduce AnyText, a diffusion-based multilingual visual text generation and editing model, that focuses on rendering accurate and coherent text in the image. AnyText comprises a diffusion pipeline with two primary elements: an auxiliary latent module and a text embedding module. The former uses inputs like text glyph, position, and masked image to generate latent features for text generation or editing. The latter employs an OCR model for encoding stroke data as embeddings, which blend with image caption embeddings from the tokenizer to generate texts that seamlessly integrate with the background. We employed text-control diffusion loss and text perceptual loss for training to further enhance writing accuracy. AnyText can write characters in multiple languages, to the best of our knowledge, this is the first work to address multilingual visual text generation. It is worth mentioning that AnyText can be plugged into existing diffusion models from the community for rendering or editing text accurately. After conducting extensive evaluation experiments, our method has outperformed all other approaches by a significant margin. Additionally, we contribute the first large-scale multilingual text images dataset, AnyWord-3M, containing 3 million image-text pairs with OCR annotations in multiple languages. Based on AnyWord-3M dataset, we propose AnyText-benchmark for the evaluation of visual text generation accuracy and quality. Our project will be open-sourced soon to improve and promote the development of text generation technology. | https://openreview.net/pdf/edddf44179f4afd7e80deb61d69c562453ed37e7.pdf |
At Which Training Stage Does Code Data Help LLMs Reasoning? | https://openreview.net/forum?id=KIPJKST4gw | https://openreview.net/forum?id=KIPJKST4gw | YINGWEI MA,Yue Liu,Yue Yu,Yuanliang Zhang,Yu Jiang,Changjian Wang,Shanshan Li | ICLR 2024,Spotlight | Large Language models (LLMs) have exhibited remarkable reasoning capabilities and become the foundation of language technologies. Inspired by the great success of code data in training LLMs, we naturally wonder at which training stage introducing code data can really help LLMs reasoning. To this end, this paper systematically explores the impact of code data on LLMs at different stages. Concretely, we introduce the code data at the pre-training stage, instruction-tuning stage, and both of them, respectively. Then, the reasoning capability of LLMs is comprehensively and fairly evaluated via six reasoning tasks. We critically analyze the experimental results and provide conclusions with insights. First, pre-training LLMs with the mixture of code and text can significantly enhance LLMs' general reasoning capability almost without negative transfer on other tasks. Besides, at the instruction-tuning stage, code data endows LLMs the task-specific reasoning capability. Moreover, the dynamic mixing strategy of code and text data assists LLMs to learn reasoning capability step-by-step during training. These insights deepen the understanding of LLMs regarding reasoning ability for their application, such as scientific question answering, legal support, etc. | https://openreview.net/pdf/ad3cc5f97b65866e7a809f7145c65de76936c8d1.pdf |
Coordinate-Aware Modulation for Neural Fields | https://openreview.net/forum?id=4UiLqimGm5 | https://openreview.net/forum?id=4UiLqimGm5 | Joo Chan Lee,Daniel Rho,Seungtae Nam,Jong Hwan Ko,Eunbyung Park | ICLR 2024,Spotlight | Neural fields, mapping low-dimensional input coordinates to corresponding signals, have shown promising results in representing various signals. Numerous methodologies have been proposed, and techniques employing MLPs and grid representations have achieved substantial success. MLPs allow compact and high expressibility, yet often suffer from spectral bias and slow convergence speed. On the other hand, methods using grids are free from spectral bias and achieve fast training speed, however, at the expense of high spatial complexity. In this work, we propose a novel way for exploiting both MLPs and grid representations in neural fields. Unlike the prevalent methods that combine them sequentially (extract features from the grids first and feed them to the MLP), we inject spectral bias-free grid representations into the intermediate features in the MLP. More specifically, we suggest a Coordinate-Aware Modulation (CAM), which modulates the intermediate features using scale and shift parameters extracted from the grid representations. This can maintain the strengths of MLPs while mitigating any remaining potential biases, facilitating the rapid learning of high-frequency components. In addition, we empirically found that the feature normalizations, which have not been successful in neural filed literature, proved to be effective when applied in conjunction with the proposed CAM. Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals. Especially in the novel view synthesis task, we achieved state-of-the-art performance with the least number of parameters and fast training speed for dynamic scenes and the best performance under 1MB memory for static scenes. CAM also outperforms the best-performing video compression methods using neural fields by a large margin. Our project page is available at https://maincold2.github.io/cam/. | https://openreview.net/pdf/0375ec72b4d2bd03dd66aa47ec6117ed73d38054.pdf |
Efficient ConvBN Blocks for Transfer Learning and Beyond | https://openreview.net/forum?id=lHZm9vNm5H | https://openreview.net/forum?id=lHZm9vNm5H | Kaichao You,Guo Qin,Anchang Bao,Meng Cao,Ping Huang,Jiulong Shan,Mingsheng Long | ICLR 2024,Spotlight | Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy. While the Train mode is indispensable for training models from scratch, the Eval mode is suitable for transfer learning and beyond, and the Deploy mode is designed for the deployment of models. This paper focuses on the trade-off between stability and efficiency in ConvBN blocks: Deploy mode is efficient but suffers from training instability; Eval mode is widely used in transfer learning but lacks efficiency. To solve the dilemma, we theoretically reveal the reason behind the diminished training stability observed in the Deploy mode. Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode. The proposed Tune mode is as stable as Eval mode for transfer learning, and its computational efficiency closely matches that of the Deploy mode. Through extensive experiments in object detection, classification, and adversarial example generation across $5$ datasets and $12$ model architectures, we demonstrate that the proposed Tune mode retains the performance while significantly reducing GPU memory footprint and training time, thereby contributing efficient ConvBN blocks for transfer learning and beyond. Our method has been integrated into both PyTorch (general machine learning framework) and MMCV/MMEngine (computer vision framework). Practitioners just need one line of code to enjoy our efficient ConvBN blocks thanks to PyTorch's builtin machine learning compilers. | https://openreview.net/pdf/da8b125b27c2e46c80329f438b05e284d75b61a8.pdf |
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior | https://openreview.net/forum?id=TrKq4Wlwcz | https://openreview.net/forum?id=TrKq4Wlwcz | Ashmit Khandelwal,Aditya Agrawal,Aanisha Bhattacharyya,Yaman Kumar,Somesh Singh,Uttaran Bhattacharya,Ishita Dasgupta,Stefano Petrangeli,Rajiv Ratn Shah,Changyou Chen,Balaji Krishnamurthy | ICLR 2024,Spotlight | Shannon and Weaver's seminal information theory divides communication into three levels: technical, semantic, and effectiveness. While the technical level deals with the accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Large Language Models (LLMs), with their wide generalizability, make some progress towards the second level. However, LLMs and other communication models are not conventionally designed for predicting and optimizing communication for desired receiver behaviors and intents. As a result, the effectiveness level remains largely untouched by modern communication systems. In this paper, we introduce the receivers' "behavior tokens," such as shares, likes, clicks, purchases, and retweets, in the LLM's training corpora to optimize content for the receivers and predict their behaviors. Other than showing similar performance to LLMs on content understanding tasks, our trained models show generalization capabilities on the behavior dimension for behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. We show results on all these capabilities using a wide range of tasks on three corpora. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior (https://behavior-in-the-wild.github.io/LCBM). | https://openreview.net/pdf/b2522b24d83cf73195d3d49d25c22b11f9633228.pdf |
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints | https://openreview.net/forum?id=2cRzmWXK9N | https://openreview.net/forum?id=2cRzmWXK9N | Chaoqi Wang,Yibo Jiang,Chenghao Yang,Han Liu,Yuxin Chen | ICLR 2024,Spotlight | The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment. Reinforcement Learning from Human Feedback (RLHF) has emerged as a promising pathway towards AI alignment but brings forth challenges due to its complexity and dependence on a separate reward model. Direct Preference Optimization (DPO) has been proposed as an alternative; and it remains equivalent to RLHF under the reverse KL regularization constraint. This paper presents $f$-DPO, a generalized approach to DPO by incorporating diverse divergence constraints. We show that under certain $f$-divergences, including Jensen-Shannon divergence, forward KL divergences and $\alpha$-divergences, the complex relationship between the reward and optimal policy can also be simplified by addressing the Karush–Kuhn–Tucker conditions. This eliminates the need for estimating the normalizing constant in the Bradley-Terry model and enables a tractable mapping between the reward function and the optimal policy. Our approach optimizes LLMs to align with human preferences in a more efficient and supervised manner under a broad set of divergence constraints. Empirically, adopting these divergences ensures a balance between alignment performance and generation diversity. Importantly, our $f$-DPO outperforms PPO-based methods in divergence efficiency, and divergence constraints directly influence expected calibration error (ECE). | https://openreview.net/pdf/161cf92d489db9ea1ae0f43eab7797245e967c2c.pdf |
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets | https://openreview.net/forum?id=CYmF38ysDa | https://openreview.net/forum?id=CYmF38ysDa | Seonghyeon Ye,Doyoung Kim,Sungdong Kim,Hyeonbin Hwang,Seungone Kim,Yongrae Jo,James Thorne,Juho Kim,Minjoon Seo | ICLR 2024,Spotlight | Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which limits interpretability since it does not consider the nature of user instructions that require instance-wise skill composition. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment Skill Sets), a fine-grained evaluation protocol for both human-based and model-based evaluation which decomposes coarse-level scoring to a skill set-level scoring for each instruction. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance and increasing the reliability of the evaluation. Using FLASK, we compare multiple open-source and proprietary LLMs and observe a high correlation between model-based and human-based evaluations. | https://openreview.net/pdf/bf031191f0c9813f740339b104d14e7a4d3f03e4.pdf |
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset | https://openreview.net/forum?id=BOfDKxfwt0 | https://openreview.net/forum?id=BOfDKxfwt0 | Lianmin Zheng,Wei-Lin Chiang,Ying Sheng,Tianle Li,Siyuan Zhuang,Zhanghao Wu,Yonghao Zhuang,Zhuohan Li,Zi Lin,Eric Xing,Joseph E. Gonzalez,Ion Stoica,Hao Zhang | ICLR 2024,Spotlight | Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m. | https://openreview.net/pdf/c90c03844055cc8a8e2b9c574d0d24cbeafe5034.pdf |
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models | https://openreview.net/forum?id=UmMa3UNDAz | https://openreview.net/forum?id=UmMa3UNDAz | Yefei He,Jing Liu,Weijia Wu,Hong Zhou,Bohan Zhuang | ICLR 2024,Spotlight | Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for low-latency real-world applications is constrained by substantial computational costs and latency issues. Quantization is a dominant way to compress and accelerate diffusion models, where post-training quantization (PTQ) and quantization-aware training (QAT) are two main approaches, each bearing its own properties. While PTQ exhibits efficiency in terms of both time and data usage, it may lead to diminished performance in low bit-width settings. On the other hand, QAT can help alleviate performance degradation but comes with substantial demands on computational and data resources. To capitalize on the advantages while avoiding their respective drawbacks, we introduce a data-free, quantization-aware and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency. Specifically, we propose a quantization-aware variant of the low-rank adapter (QALoRA) that can be merged with model weights and jointly quantized to low bit-width. The fine-tuning process distills the denoising capabilities of the full-precision model into its quantized counterpart, eliminating the requirement for training data. To further enhance performance, we introduce scale-aware optimization to address ineffective learning of QALoRA due to variations in weight quantization scales across different layers. We also employ temporal learned step-size quantization to handle notable variations in activation distributions across denoising steps. Extensive experimental results demonstrate that our method significantly outperforms previous PTQ-based diffusion models while maintaining similar time and data efficiency. Specifically, there is only a marginal $0.05$ sFID increase when quantizing both weights and activations of LDM-4 to 4-bit on ImageNet $256\times256$. Compared to QAT-based methods, our EfficientDM also boasts a $16.2\times$ faster quantization speed with comparable generation quality, rendering it a compelling choice for practical applications. | https://openreview.net/pdf/ee376b0254ced2d4b2ed0b77eec1b7f0a73d0b01.pdf |
BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models | https://openreview.net/forum?id=3TO3TtnOFl | https://openreview.net/forum?id=3TO3TtnOFl | Qingqing Cao,Sewon Min,Yizhong Wang,Hannaneh Hajishirzi | ICLR 2024,Spotlight | Retrieval augmentation addresses many critical problems in large language models such as hallucination, staleness, and privacy leaks.
However, running retrieval-augmented language models (LMs) is slow and difficult to scale due to processing large amounts of retrieved text.
We introduce binary token representations (BTR), which use 1-bit vectors to precompute every token in passages, significantly reducing computation during inference.
Despite the potential loss of accuracy, our new calibration techniques and training objectives restore performance. Combined with offline and runtime compression, this only requires 127GB of disk space for encoding 3 billion tokens in Wikipedia.
Our experiments show that on five knowledge-intensive NLP tasks, BTR accelerates state-of-the-art inference by up to 4x and reduces storage by over 100x while maintaining over 95% task performance. Our code is publicly available at https://github.com/csarron/BTR. | https://openreview.net/pdf/e8e8dce96bfa93af6f7cc3520631eb3db8e8d919.pdf |
Frozen Transformers in Language Models Are Effective Visual Encoder Layers | https://openreview.net/forum?id=t0FI3Q66K5 | https://openreview.net/forum?id=t0FI3Q66K5 | Ziqi Pang,Ziyang Xie,Yunze Man,Yu-Xiong Wang | ICLR 2024,Spotlight | This paper reveals that large language models (LLMs), despite being trained solely on text data, are surprisingly}strong encoders for purely visual tasks in the absence of language. Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -- employing a frozen transformer block from pre-trained LLMs as a constituent encoder layer to directly process visual tokens. Our work pushes the boundaries of leveraging LLMs for computer vision tasks, significantly departing from conventional practices that typically necessitate a multi-modal vision-language setup with associated language prompts, inputs, or outputs. We demonstrate that our approach consistently enhances performance across a diverse range of tasks} encompassing pure 2D or 3D visual recognition tasks (e.g., image and point cloud classification), temporal modeling tasks (e.g., action recognition), non-semantic tasks (e.g., motion forecasting), and multi-modal tasks (e.g., 2D/3D visual question answering and image-text retrieval). Such improvements are a general phenomenon, applicable to various types of LLMs (e.g., LLaMA and OPT) and different LLM transformer blocks. We additionally propose the information filtering hypothesis to explain the effectiveness of pre-trained LLMs in visual encoding -- the pre-trained LLM transformer blocks discern informative visual tokens and further amplify their effect. This hypothesis is empirically supported by the observation that the feature activation, after training with LLM transformer blocks, exhibits a stronger focus on relevant regions. We hope that our work inspires new perspectives on utilizing LLMs and deepening our understanding of their underlying mechanisms. | https://openreview.net/pdf/9c46b8dad8775df26907aed1cec97a61b8c7d014.pdf |
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series | https://openreview.net/forum?id=s9z0HzWJJp | https://openreview.net/forum?id=s9z0HzWJJp | Junyan Cheng,Peter Chin | ICLR 2024,Spotlight | We introduce SocioDojo, an open-ended lifelong learning environment for developing ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics such as economics, finance, politics, and culture. It consists of (1) information sources from news, social media, reports, etc., (2) a knowledge base built from books, journals, and encyclopedias, plus a toolbox of Internet and knowledge graph search interfaces, (3) 30K high-quality time series in finance, economy, society, and polls, which support a novel task called "hyperportfolio", that can reliably and scalably evaluate societal analysis and decision-making power of agents, inspired by portfolio optimization with time series as assets to "invest". We also propose a novel Analyst-Assistant-Actuator architecture for the hyperportfolio task, and a Hypothesis & Proof prompting for producing in-depth analyses on input news, articles, etc. to assist decision-making. We perform experiments and ablation studies to explore the factors that impact performance. The results show that our proposed method achieves improvements of 32.4% and 30.4% compared to the state-of-the-art method in the two experimental settings. | https://openreview.net/pdf/473beb76f232cb31d5e4d7594a17375a1347cef9.pdf |
Learning Performance-Improving Code Edits | https://openreview.net/forum?id=ix7rLVHXyY | https://openreview.net/forum?id=ix7rLVHXyY | Alexander G Shypula,Aman Madaan,Yimeng Zeng,Uri Alon,Jacob R. Gardner,Yiming Yang,Milad Hashemi,Graham Neubig,Parthasarathy Ranganathan,Osbert Bastani,Amir Yazdanbakhsh | ICLR 2024,Spotlight | With the decline of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes remain elusive due to the difficulty of understanding the semantics of code. Simultaneously, pretrained large language models (LLMs) have demonstrated strong capabilities at solving a wide range of programming tasks. To that end, we introduce a framework for adapting LLMs to high-level program optimization. First, we curate a dataset of performance-improving edits made by human programmers of over 77,000 competitive C++ programming submission pairs, accompanied by extensive unit tests. A major challenge is the significant variability of measuring performance on commodity hardware, which can lead to spurious "improvements." To isolate and reliably evaluate the impact of program optimizations, we design an environment based on the gem5 full system simulator, the de facto simulator used in academia and industry. Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play. A combination of these techniques achieves a mean speedup of 6.86$\times$ with eight generations, higher than average optimizations from individual programmers (3.66$\times$). Using our model's fastest generations, we set a new upper limit on the fastest speedup possible for our dataset at 9.64$\times$ compared to using the fastest human submissions available (9.56$\times$). | https://openreview.net/pdf/e94f139ce25197392e4252dc56ed557ff74094fd.pdf |
Quasi-Monte Carlo for 3D Sliced Wasserstein | https://openreview.net/forum?id=Wd47f7HEXg | https://openreview.net/forum?id=Wd47f7HEXg | Khai Nguyen,Nicola Bariletto,Nhat Ho | ICLR 2024,Spotlight | Monte Carlo (MC) integration has been employed as the standard approximation method for the Sliced Wasserstein (SW) distance, whose analytical expression involves an intractable expectation. However, MC integration is not optimal in terms of absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically evaluate various methods to construct QMC point sets on the 3D unit-hypersphere, including the Gaussian-based and equal area mappings, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimator for stochastic optimization, we extend QSW to Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness in the discussed point sets. Theoretically, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants. | https://openreview.net/pdf/f68a8423256e1b343e90eb5d17ebb7636561a6fa.pdf |
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs | https://openreview.net/forum?id=l3qtSNsPvC | https://openreview.net/forum?id=l3qtSNsPvC | Thien Le,Luana Ruiz,Stefanie Jegelka | ICLR 2024,Spotlight | Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on graphs is nontrivial as graphs are non-Euclidean. Existing graph sampling techniques require not only computing the spectra of large matrices but also repeating these computations when the graph changes, e.g., grows. In this paper, we introduce a signal sampling theory for a type of graph limit---the graphon. We prove a Poincaré inequality for graphon signals and show that complements of node subsets satisfying this inequality are unique sampling sets for Paley-Wiener spaces of graphon signals. Exploiting connections with spectral clustering and Gaussian elimination, we prove that such sampling sets are consistent in the sense that unique sampling sets on a convergent graph sequence converge to unique sampling sets on the graphon. We then propose a related graphon signal sampling algorithm for large graphs, and demonstrate its good empirical performance on graph machine learning tasks. | https://openreview.net/pdf/787ddf1932fd5fa37f701cc1319cf90eb55d942b.pdf |
Cascading Reinforcement Learning | https://openreview.net/forum?id=KjOAHlKMF5 | https://openreview.net/forum?id=KjOAHlKMF5 | Yihan Du,R. Srikant,Wei Chen | ICLR 2024,Spotlight | Cascading bandits have gained popularity in recent years due to their applicability to recommendation systems and online advertising. In the cascading bandit model, at each timestep, an agent recommends an ordered subset of items (called an item list) from a pool of items, each associated with an unknown attraction probability. Then, the user examines the list, and clicks the first attractive item (if any), and after that, the agent receives a reward. The goal of the agent is to maximize the expected cumulative reward. However, the prior literature on cascading bandits ignores the influences of user states (e.g., historical behaviors) on recommendations and the change of states as the session proceeds. Motivated by this fact, we propose a generalized cascading RL framework, which considers the impact of user states and state transition into decisions. In cascading RL, we need to select items not only with large attraction probabilities but also leading to good successor states. This imposes a huge computational challenge due to the combinatorial action space. To tackle this challenge, we delve into the properties of value functions, and design an oracle BestPerm to efficiently find the optimal item list. Equipped with BestPerm, we develop two algorithms CascadingVI and CascadingBPI, which are both computationally-efficient and sample-efficient, and provide near-optimal regret and sample complexity guarantees. Furthermore, we present experiments to show the improved computational and sample efficiencies of our algorithms compared to straightforward adaptations of existing RL algorithms in practice. | https://openreview.net/pdf/831efaa7342d514569de4663496b0ff76a04015a.pdf |
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities | https://openreview.net/forum?id=S5aUhpuyap | https://openreview.net/forum?id=S5aUhpuyap | Benjamin S. H. Lyo,Cristina Savin | ICLR 2024,Spotlight | Despite many successful examples in which probabilistic inference can account for perception, we have little understanding of how the brain represents and uses structured priors that capture the complexity of natural input statistics. Here we construct a recurrent circuit model that can implicitly represent priors over latent variables, and combine them with sensory and contextual sources of information to encode task-specific posteriors. Inspired by the recent success of diffusion models as means of learning and using priors over images, our model uses dendritic nonlinearities optimized for denoising, and stochastic somatic integration with the degree of noise modulated by an oscillating global signal. Combining these elements into a recurrent network yields a stochastic dynamical system that samples from the prior at a rate prescribed by the period of the global oscillator. Additional inputs reflecting sensory or top-down contextual information alter these dynamics to generate samples from the corresponding posterior, with different input gating patterns selecting different inference tasks. We demonstrate that this architecture can sample from low dimensional nonlinear manifolds and multimodal posteriors. Overall, the model provides a new framework for circuit-level representation of probabilistic information, in a format that facilitates flexible inference. | https://openreview.net/pdf/70e72bd9cb947d915efc4ad99a0246f77307c5c0.pdf |
On the hardness of learning under symmetries | https://openreview.net/forum?id=ARPrtuzAnQ | https://openreview.net/forum?id=ARPrtuzAnQ | Bobak Kiani,Thien Le,Hannah Lawrence,Stefanie Jegelka,Melanie Weber | ICLR 2024,Spotlight | We study the problem of learning equivariant neural networks via gradient descent. The incorporation of known symmetries ("equivariance") into neural nets has empirically improved the performance of learning pipelines, in domains ranging from biology to computer vision. However, a rich yet separate line of learning theoretic research has demonstrated that actually learning shallow, fully-connected (i.e. non-symmetric) networks has exponential complexity in the correlational statistical query (CSQ) model, a framework encompassing gradient descent. In this work, we ask: are known problem symmetries sufficient to alleviate the fundamental hardness of learning neural nets with gradient descent? We answer this question in the negative. In particular, we give lower bounds for shallow graph neural networks, convolutional networks, invariant polynomials, and frame-averaged networks for permutation subgroups, which all scale either superpolynomially or exponentially in the relevant input dimension. Therefore, in spite of the significant inductive bias imparted via symmetry, actually learning the complete classes of functions represented by equivariant neural networks via gradient descent remains hard. | https://openreview.net/pdf/051e80193269101cefc8987b52aae50357e5f33d.pdf |
An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models | https://openreview.net/forum?id=nc5GgFAvtk | https://openreview.net/forum?id=nc5GgFAvtk | Haochen Luo,Jindong Gu,Fengyuan Liu,Philip Torr | ICLR 2024,Spotlight | Different from traditional task-specific vision models, recent large VLMs can readily adapt to different vision tasks by simply using different textual instructions, i.e., prompts. However, a well-known concern about traditional task-specific vision models is that they can be misled by imperceptible adversarial perturbations. Furthermore, the concern is exacerbated by the phenomenon that the same adversarial perturbations can fool different task-specific models. Given that VLMs rely on prompts to adapt to different tasks, an intriguing question emerges: Can a single adversarial image mislead all predictions of VLMs when a thousand different prompts are given? This question essentially introduces a novel perspective on adversarial transferability: cross-prompt adversarial transferability. In this work, we propose the Cross-Prompt Attack (CroPA). This proposed method updates the visual adversarial perturbation with learnable textual prompts, which are designed to counteract the misleading effects of the adversarial image. By doing this, CroPA significantly improves the transferability of adversarial examples across prompts. Extensive experiments are conducted to verify the strong cross-prompt adversarial transferability of CroPA with prevalent VLMs including Flamingo, BLIP-2, and InstructBLIP in various different tasks. | https://openreview.net/pdf/67d91f245eccef09295bf6fe4b1cc4a142c6c4d5.pdf |
One For All: Towards Training One Graph Model For All Classification Tasks | https://openreview.net/forum?id=4IT2pgc9v6 | https://openreview.net/forum?id=4IT2pgc9v6 | Hao Liu,Jiarui Feng,Lecheng Kong,Ningyue Liang,Dacheng Tao,Yixin Chen,Muhan Zhang | ICLR 2024,Spotlight | Designing a single model to address multiple tasks has been a long-standing objective in artificial intelligence. Recently, large language models have demonstrated exceptional capability in solving different tasks within the language domain. However, a unified model for various graph tasks remains underexplored, primarily due to the challenges unique to the graph learning domain. First, graph data from different areas carry distinct attributes and follow different distributions. Such discrepancy makes it hard to represent graphs in a single representation space. Second, tasks on graphs diversify into node, link, and graph tasks, requiring distinct embedding strategies. Finally, an appropriate graph prompting paradigm for in-context learning is unclear. We propose **One for All (OFA)**, the first general framework that can use a single graph model to address the above challenges. Specifically, OFA proposes text-attributed graphs to unify different graph data by describing nodes and edges with natural language and uses language models to encode the diverse and possibly cross-domain text attributes to feature vectors in the same embedding space. Furthermore, OFA introduces the concept of nodes-of-interest to standardize different tasks with a single task representation. For in-context learning on graphs, OFA introduces a novel graph prompting paradigm that appends prompting substructures to the input graph, which enables it to address varied tasks without fine-tuning. We train the OFA model using graph data from multiple domains (including citation networks, molecular graphs, knowledge graphs, etc.) simultaneously and evaluate its ability in supervised, few-shot, and zero-shot learning scenarios. OFA performs well across different tasks, making it the first general-purpose across-domains classification model on graphs. | https://openreview.net/pdf/390d857f1c69a3defd601a75fb78273100d01927.pdf |
$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation | https://openreview.net/forum?id=wg8NPfeMF9 | https://openreview.net/forum?id=wg8NPfeMF9 | Yining Jiao,Carlton Jude ZDANSKI,Julia S Kimbell,Andrew Prince,Cameron P Worden,Samuel Kirse,Christopher Rutter,Benjamin Shields,William Alexander Dunn,Jisan Mahmud,Marc Niethammer | ICLR 2024,Spotlight | Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate. Such a method would be of high utility to researchers to discover knowledge hidden in a population of shapes. For scientific shape discovery purpose, we propose a 3D Neural Additive Model for Interpretable Shape Representation ($\texttt{NAISR}$) which describes individual shapes by deforming a shape atlas in accordance to the effect of disentangled covariates. Our approach captures shape population trends and allows for patient-specific predictions through shape transfer. $\texttt{NAISR}$ is the first approach to combine the benefits of deep implicit shape representations with an atlas deforming according to specified covariates. We evaluate $\texttt{NAISR}$ with respect to shape reconstruction, shape disentanglement, shape evolution, and shape transfer on three datasets, i.e. 1) $\textit{Starman}$, a simulated 2D shape dataset; 2) ADNI hippocampus 3D shape dataset; 3) pediatric airway 3D shape dataset. Our experiments demonstrate that $\texttt{NAISR}$ achieves competitive shape reconstruction performance while retaining interpretability. Our code is available at https://github.com/uncbiag/NAISR. | https://openreview.net/pdf/4ac4d43849312f67923cbf9fd40290bebb68e6ca.pdf |
Feature emergence via margin maximization: case studies in algebraic tasks | https://openreview.net/forum?id=i9wDX850jR | https://openreview.net/forum?id=i9wDX850jR | Depen Morwani,Benjamin L. Edelman,Costin-Andrei Oncescu,Rosie Zhao,Sham M. Kakade | ICLR 2024,Spotlight | Understanding the internal representations learned by neural networks is a cornerstone challenge in the science of machine learning. While there have been significant recent strides in some cases towards understanding *how* neural networks implement specific target functions, this paper explores a complementary question -- *why* do networks arrive at particular computational strategies?
Our inquiry focuses on the algebraic learning tasks of modular addition, sparse parities, and finite group operations. Our primary theoretical findings analytically characterize the features learned by stylized neural networks for these algebraic tasks. Notably, our main technique demonstrates how the principle of margin maximization alone can be used to fully specify the features learned by the network.
Specifically, we prove that the trained networks utilize Fourier features to perform modular addition and employ features corresponding to irreducible group-theoretic representations to perform compositions in general groups, aligning closely with the empirical observations of Nanda et al. (2023) and Chughtai et al. (2023). More generally, we hope our techniques can help to foster a deeper understanding of why neural networks adopt specific computational strategies. | https://openreview.net/pdf/fdd05f82a84d50198e42915f002f4df0aefac612.pdf |
On the Stability of Iterative Retraining of Generative Models on their own Data | https://openreview.net/forum?id=JORAfH2xFd | https://openreview.net/forum?id=JORAfH2xFd | Quentin Bertrand,Joey Bose,Alexandre Duplessis,Marco Jiralerspong,Gauthier Gidel | ICLR 2024,Spotlight | Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical human's ability to discern the authenticity of samples. Undeniably, a key driver of this success is enabled by the massive amounts of web-scale data consumed by these models. Due to these models' striking performance and ease of availability, the web will inevitably be increasingly populated with synthetic content. Such a fact directly implies that future iterations of generative models will be trained on both clean and artificially generated data from past models. In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets---from classical training on real data to self-consuming generative models trained on purely synthetic data. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough and the proportion of clean training data (w.r.t. synthetic data) is large enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models on CIFAR10 and FFHQ. | https://openreview.net/pdf/119c01132e3767a01a8777074185c4c7f8fb3824.pdf |
Intriguing Properties of Generative Classifiers | https://openreview.net/forum?id=rmg0qMKYRQ | https://openreview.net/forum?id=rmg0qMKYRQ | Priyank Jaini,Kevin Clark,Robert Geirhos | ICLR 2024,Spotlight | What is the best paradigm to recognize objects---discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more robust)? We build on recent advances in generative modeling that turn text-to-image models into classifiers. This allows us to study their behavior and to compare them against discriminative models and human psychophysical data.
We report four intriguing emergent properties of generative classifiers: they show a record-breaking human-like shape bias (99% for Imagen), near human-level out-of-distribution accuracy, state-of-the-art alignment with human classification errors, and they understand certain perceptual illusions. Our results indicate that while the current dominant paradigm for modeling human object recognition is discriminative inference, zero-shot generative models approximate human object recognition data surprisingly well. | https://openreview.net/pdf/2eb2ae04198b3791439cc178f097c96bc9aceb8a.pdf |
Fast Imitation via Behavior Foundation Models | https://openreview.net/forum?id=qnWtw3l0jb | https://openreview.net/forum?id=qnWtw3l0jb | Matteo Pirotta,Andrea Tirinzoni,Ahmed Touati,Alessandro Lazaric,Yann Ollivier | ICLR 2024,Spotlight | Imitation learning (IL) aims at producing agents that can imitate any behavior given a few expert demonstrations. Yet existing approaches require many demonstrations and/or running (online or offline) reinforcement learning (RL) algorithms for each new imitation task. Here we show that recent RL foundation models based on successor measures can imitate any expert behavior almost instantly with just a few demonstrations and no need for RL or fine-tuning, while accommodating several IL principles (behavioral cloning, feature matching, reward-based, and goal-based reductions). In our experiments, imitation via RL foundation models matches, and often surpasses, the performance of SOTA offline IL algorithms, and produces imitation policies from new demonstrations within seconds instead of hours. | https://openreview.net/pdf/5110a85131511658c1a9003bcf050afee966a8fc.pdf |
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning | https://openreview.net/forum?id=zSxpnKh1yS | https://openreview.net/forum?id=zSxpnKh1yS | Yucheng Yang,Tianyi Zhou,Qiang He,Lei Han,Mykola Pechenizkiy,Meng Fang | ICLR 2024,Spotlight | Unsupervised reinforcement learning (URL) aims to learn general skills for unseen downstream tasks. Mutual Information Skill Learning (MISL) addresses URL by maximizing the mutual information between states and skills but lacks sufficient theoretical analysis, e.g., how well its learned skills can initialize a downstream task's policy. Our new theoretical analysis shows that the diversity and separatability of learned skills are fundamentally critical to downstream task adaptation but MISL does not necessarily guarantee them. To improve MISL, we propose a novel disentanglement metric LSEPIN and build an information-geometric connection between LSEPIN and downstream task adaptation cost. For better geometric properties, we investigate a new strategy that replaces the KL divergence in information geometry with Wasserstein distance. We extend the geometric analysis to it, which leads to a novel skill-learning objective WSEP. It is theoretically justified to be helpful to task adaptation and it is capable of discovering more initial policies for downstream tasks than MISL. We further propose a Wasserstein distance-based algorithm PWSEP can theoretically discover all potentially optimal initial policies. | https://openreview.net/pdf/368c8ed5d5f3738709bdeefccb59dd4ed83ece35.pdf |
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling | https://openreview.net/forum?id=sLdVl0q68X | https://openreview.net/forum?id=sLdVl0q68X | Kun Wang,Hao Wu,Yifan Duan,Guibin Zhang,Kai Wang,Xiaojiang Peng,Yu Zheng,Yuxuan Liang,Yang Wang | ICLR 2024,Spotlight | Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of data and the high costs associated with deploying sensors lead to significant data imbalances. Models that are overly tailored and lack causal relationships further compromise the generalizabilities of inference methods. Towards this end, we first establish a causal concept for ST predictions, named NuwaDynamics, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Concretely, we initially leverage upstream self-supervision to discern causal important patches, imbuing the model with generalized information and conducting informed interventions on complementary trivial patches to extrapolate potential test distributions. This phase is referred to as the discovery step. Advancing beyond discovery step, we transfer the data to downstream tasks for targeted ST objectives, aiding the model in recognizing a broader potential distribution and fostering its causal perceptual capabilities (refer as Update step). Our concept aligns seamlessly with the contemporary backdoor adjustment mechanism in causality theory. Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the NuwaDynamics concept. NuwaDynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions. | https://openreview.net/pdf/7c14ae2b67a7d27b177a742d38945097c4eda38c.pdf |
Pre-Training and Fine-Tuning Generative Flow Networks | https://openreview.net/forum?id=ylhiMfpqkm | https://openreview.net/forum?id=ylhiMfpqkm | Ling Pan,Moksh Jain,Kanika Madan,Yoshua Bengio | ICLR 2024,Spotlight | Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects from a given unnormalized reward distribution.
They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks. However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks.
Inspired by recent successes of unsupervised pre-training in various domains, we introduce a novel approach for reward-free pre-training of GFlowNets. By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to explore the candidate space. Specifically, OC-GFN learns to reach any targeted outcomes, akin to goal-conditioned policies in reinforcement learning.
We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks.
Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an intractable marginalization over possible outcomes. We propose a novel way to approximate this marginalization by learning an amortized predictor enabling efficient fine-tuning.
Extensive experimental results validate the efficacy of our approach, demonstrating the effectiveness of pre-training the OC-GFN, and its ability to swiftly adapt to downstream tasks and discover modes more efficiently.
This work may serve as a foundation for further exploration of pre-training strategies in the context of GFlowNets. | https://openreview.net/pdf/4b4bd3338aba282409e92ec103c74997f2ab2c8f.pdf |
CO2: Efficient Distributed Training with Full Communication-Computation Overlap | https://openreview.net/forum?id=ZO5cn4IfaN | https://openreview.net/forum?id=ZO5cn4IfaN | Weigao Sun,Zhen Qin,Weixuan Sun,Shidi Li,Dong Li,Xuyang Shen,Yu Qiao,Yiran Zhong | ICLR 2024,Spotlight | The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-performance cluster featuring high-speed communication interconnectivity is prohibitively costly, and accessible only to prominent entities. In this work, we aim to lower this barrier and democratize large-scale training with limited bandwidth clusters. We propose a new approach called CO2 that introduces local-updating and asynchronous communication to the distributed data-parallel training, thereby facilitating the full overlap of COmmunication with COmputation. CO2 is able to attain a high scalability even on extensive multi-node clusters constrained by very limited communication bandwidth. We further propose the staleness gap penalty and outer momentum clipping techniques together with CO2 to bolster its convergence and training stability. Besides, CO2 exhibits seamless integration with well-established ZeRO-series optimizers which mitigate memory consumption of model states with large model training. We also provide a mathematical proof of convergence, accompanied by the establishment of a stringent upper bound. Furthermore, we validate our findings through an extensive set of practical experiments encompassing a wide range of tasks in the fields of computer vision and natural language processing. These experiments serve to demonstrate the capabilities of CO2 in terms of convergence, generalization, and scalability when deployed across configurations comprising up to 128 A100 GPUs. The outcomes emphasize the outstanding capacity of CO2 to hugely improve scalability, no matter on clusters with 800Gbps RDMA or 80Gbps TCP/IP inter-node connections. | https://openreview.net/pdf/81103680cdf97ee7c0afa17e78b1b069b3facde6.pdf |
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling | https://openreview.net/forum?id=zMoNrajk2X | https://openreview.net/forum?id=zMoNrajk2X | Seyedmorteza Sadat,Jakob Buhmann,Derek Bradley,Otmar Hilliges,Romann M. Weber | ICLR 2024,Spotlight | While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets. We attribute this problem to the role of the conditioning signal in inference and offer an improved sampling strategy for diffusion models that can increase generation diversity, especially at high guidance scales, with minimal loss of sample quality. Our sampling strategy anneals the conditioning signal by adding scheduled, monotonically decreasing Gaussian noise to the conditioning vector during inference to balance diversity and condition alignment. Our Condition-Annealed Diffusion Sampler (CADS) can be used with any pretrained model and sampling algorithm, and we show that it boosts the diversity of diffusion models in various conditional generation tasks. Further, using an existing pretrained diffusion model, CADS achieves a new state-of-the-art FID of 1.70 and 2.31 for class-conditional ImageNet generation at 256$\times$256 and 512$\times$512 respectively. | https://openreview.net/pdf/7673e3515afc0d2540e2ff2de4f253aaa56742e7.pdf |
Image Inpainting via Iteratively Decoupled Probabilistic Modeling | https://openreview.net/forum?id=rUf9G9k2im | https://openreview.net/forum?id=rUf9G9k2im | Wenbo Li,Xin Yu,Kun Zhou,Yibing Song,Zhe Lin | ICLR 2024,Spotlight | Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions. In contrast, iterative probabilistic algorithms, such as autoregressive and denoising diffusion models, have to be deployed with massive computing resources for decent effect. To achieve high-quality results with low computational cost, we present a novel pixel spread model (PSM) that iteratively employs decoupled probabilistic modeling, combining the optimization efficiency of GANs with the prediction tractability of probabilistic models. As a result, our model selectively spreads informative pixels throughout the image in a few iterations, largely enhancing the completion quality and efficiency. On multiple benchmarks, we achieve new state-of-the-art performance. Our code and models will be publicly available. | https://openreview.net/pdf/1e81620ef68cdb56dc5ca52bc4fa349c9b1ec33b.pdf |
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval | https://openreview.net/forum?id=5BXAXOpaWu | https://openreview.net/forum?id=5BXAXOpaWu | Yongchao Du,Min Wang,Wengang Zhou,Shuping Hui,Houqiang Li | ICLR 2024,Spotlight | The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent.
Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments when deploying the large vision-language model. To tackle the above problems, we propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning. In the framework, we propose a new adaptive token learner that maps an image to a sentence in the word embedding space of VL model. The sentence adaptively captures discriminative visual information and is further integrated with the text modifier. An asymmetric structure is devised for flexible deployment, in which the lightweight model is adopted for the query side while the large VL model is deployed on the gallery side. The global contrastive distillation and the local alignment regularization are adopted for the alignment between the light model and the VL model for CIR task. Our experiments demonstrate that the proposed ISA could better cope with the real retrieval scenarios and further improve retrieval accuracy and efficiency. | https://openreview.net/pdf/3668aaf0e113e66d0632d7fdc028f3f514a39db5.pdf |
Bespoke Solvers for Generative Flow Models | https://openreview.net/forum?id=1PXEY7ofFX | https://openreview.net/forum?id=1PXEY7ofFX | Neta Shaul,Juan Perez,Ricky T. Q. Chen,Ali Thabet,Albert Pumarola,Yaron Lipman | ICLR 2024,Spotlight | Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs) which require a large Number of Function Evaluations (NFE) to approximate well. Existing methods to alleviate the costly sampling process include model distillation and designing dedicated ODE solvers. However, distillation is costly to train and sometimes can deteriorate quality, while dedicated solvers still require relatively large NFE to produce high quality samples. In this paper we introduce ``Bespoke solvers'', a novel framework for constructing custom ODE solvers tailored to the ODE of a given pre-trained flow model. Our approach optimizes an order consistent and parameter-efficient solver (e.g., with 80 learnable parameters), is trained for roughly 1\% of the GPU time required for training the pre-trained model, and significantly improves approximation and generation quality compared to dedicated solvers. For example, a Bespoke solver for a CIFAR10 model produces samples with Fréchet Inception Distance (FID) of 2.73 with 10 NFE, and gets to 1\% of the Ground Truth (GT) FID (2.59) for this model with only 20 NFE. On the more challenging ImageNet-64$\times$64, Bespoke samples at 2.2 FID with 10 NFE, and gets within 2\% of GT FID (1.71) with 20 NFE. | https://openreview.net/pdf/aa7d0f5148157773b366fcd27b4a30eda6f077b9.pdf |
Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning | https://openreview.net/forum?id=YCPDFfmkFr | https://openreview.net/forum?id=YCPDFfmkFr | Antoine Bambade,Fabian Schramm,Adrien Taylor,Justin Carpentier | ICLR 2024,Spotlight | Optimization layers within neural network architectures have become increasingly popular for their ability to solve a wide range of machine learning tasks and to model domain-specific knowledge. However, designing optimization layers requires careful consideration as the underlying optimization problems might be infeasible during training.
Motivated by applications in learning, control and robotics, this work focuses on convex quadratic programming (QP) layers. The specific structure of this type of optimization layer can be efficiently exploited for faster computations while still allowing rich modeling capabilities. We leverage primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QP solutions.
More precisely, we propose a unified approach which tackles the differentiability of the closest feasible QP solutions in a classical $\ell_2$ sense. We then harness this approach to enrich the expressive capabilities of existing QP layers. More precisely, we show how differentiating through infeasible QPs during training enables to drive towards feasibility at test time a new range of QP layers. These layers notably demonstrate superior predictive performance in some conventional learning tasks. Additionally, we present alternative formulations that enhance numerical robustness, speed, and accuracy for training such layers.
Along with these contributions, we provide an open-source C++ software package called QPLayer for differentiating feasible and infeasible convex QPs and which can be interfaced with modern learning frameworks. | https://openreview.net/pdf/731de6c558997b87a5381292530348a26e31fb5f.pdf |
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers | https://openreview.net/forum?id=TzoHLiGVMo | https://openreview.net/forum?id=TzoHLiGVMo | Stéphane d'Ascoli,Sören Becker,Philippe Schwaller,Alexander Mathis,Niki Kilbertus | ICLR 2024,Spotlight | We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory. We perform extensive evaluations on two datasets: (i) the existing ‘Strogatz’ dataset featuring two-dimensional systems; (ii) ODEBench, a collection of one- to four-dimensional systems that we carefully curated from the literature to provide a more holistic benchmark. ODEFormer consistently outperforms existing methods while displaying substantially improved robustness to noisy and irregularly sampled observations, as well as faster inference. We release our code, model and benchmark at https://github.com/sdascoli/odeformer. | https://openreview.net/pdf/f91ca392113614803487b965a914d26be9910ccf.pdf |
Convergence of Bayesian Bilevel Optimization | https://openreview.net/forum?id=fLXpXa7iiz | https://openreview.net/forum?id=fLXpXa7iiz | Shi Fu,Fengxiang He,Xinmei Tian,Dacheng Tao | ICLR 2024,Spotlight | This paper presents the first theoretical guarantee for Bayesian bilevel optimization (BBO) that we term for the prevalent bilevel framework combining Bayesian optimization at the outer level to tune hyperparameters, and the inner-level stochastic gradient descent (SGD) for training the model. We prove sublinear regret bounds suggesting simultaneous convergence of the inner-level model parameters and outer-level hyperparameters to optimal configurations for generalization capability. A pivotal, technical novelty in the proofs is modeling the excess risk of the SGD-trained parameters as evaluation noise during Bayesian optimization. Our theory implies the inner unit horizon, defined as the number of SGD iterations, shapes the convergence behavior of BBO. This suggests practical guidance on configuring the inner unit horizon to enhance training efficiency and model performance. | https://openreview.net/pdf/209c3efe9f38c1f8a12c87cee69ec4513e0f8b8d.pdf |
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field | https://openreview.net/forum?id=QQ6RgKYiQq | https://openreview.net/forum?id=QQ6RgKYiQq | Kaizhi Yang,Xiaoshuai Zhang,Zhiao Huang,Xuejin Chen,Zexiang Xu,Hao Su | ICLR 2024,Spotlight | We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on the same part share the common motion pattern. From the perspective of fluid simulation, existing deformation-based methods for dynamic NeRF can be seen as parameterizing the scene motion under the Eulerian view, i.e., focusing on specific locations in space through which the fluid flows as time passes. However, it is intractable to extract the motion of constituting objects or parts using the Eulerian view representation. In this work, we introduce the dual Lagrangian view and enforce representations under the Eulerian/Lagrangian views to be cycle-consistent. Under the Lagrangian view, we parameterize the scene motion by tracking the trajectory of particles on objects. The Lagrangian view makes it convenient to discover parts by factorizing the scene motion as a composition of part-level rigid motions. Experimentally, our method can achieve fast and high-quality dynamic scene reconstruction from even a single moving camera, and the induced part-based representation allows direct applications of part tracking, animation, 3D scene editing, etc. | https://openreview.net/pdf/5a09f0c81c6bf7abc3ea9cd5d6138c06f4812622.pdf |
Equivariant Matrix Function Neural Networks | https://openreview.net/forum?id=yrgQdA5NkI | https://openreview.net/forum?id=yrgQdA5NkI | Ilyes Batatia,Lars Leon Schaaf,Gabor Csanyi,Christoph Ortner,Felix Andreas Faber | ICLR 2024,Spotlight | Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MPNNs face challenges when modeling non-local interactions in systems such as large conjugated molecules, metals, or amorphous materials.
Although Spectral GNNs and traditional neural networks such as recurrent neural networks and transformers mitigate these challenges, they often lack extensivity, adaptability, generalizability, computational efficiency, or fail to capture detailed structural relationships or symmetries in the data. To address these concerns, we introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through analytic matrix equivariant functions. Employing resolvent expansions offers a straightforward implementation and the potential for linear scaling with system size.
The MFN architecture achieves state-of-the-art performance in standard graph benchmarks, such as the ZINC and TU datasets, and is able to capture intricate non-local interactions in quantum systems. The code and the datasets will be made public. | https://openreview.net/pdf/f769ed973540097e6a721fc0b64e4271088eabe0.pdf |
Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction | https://openreview.net/forum?id=kUuKFW7DIF | https://openreview.net/forum?id=kUuKFW7DIF | Jiatong Shi,Hirofumi Inaguma,Xutai Ma,Ilia Kulikov,Anna Sun | ICLR 2024,Spotlight | Existing Self-Supervised Learning (SSL) models for speech typically process speech signals at a fixed resolution of 20 milliseconds. This approach overlooks the varying informational content present at different resolutions in speech signals. In contrast, this paper aims to incorporate multi-resolution information into speech self-supervised representation learning. We introduce an SSL model that leverages a hierarchical Transformer architecture, complemented by HuBERT-style masked prediction objectives, to process speech at multiple resolutions. Experimental results indicate that the proposed model not only achieves more efficient inference but also exhibits superior or comparable performance to the original HuBERT model over various tasks. Specifically, significant performance improvements over the original HuBERT have been observed in fine-tuning experiments on the LibriSpeech speech recognition benchmark as well as in evaluations using the Speech Universal PERformance Benchmark (SUPERB) and Multilingual SUPERB (ML-SUPERB). | https://openreview.net/pdf/c0f81178cf7aa97eb93f18dd2672018c8ab232b3.pdf |
Input-gradient space particle inference for neural network ensembles | https://openreview.net/forum?id=nLWiR5P3wr | https://openreview.net/forum?id=nLWiR5P3wr | Trung Trinh,Markus Heinonen,Luigi Acerbi,Samuel Kaski | ICLR 2024,Spotlight | Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based variational inference (ParVI) methods enhance diversity by formalizing a repulsion term based on a network similarity kernel. However, weight-space repulsion is inefficient due to over-parameterization, while direct function-space repulsion has been found to produce little improvement over DEs. To sidestep these difficulties, we propose First-order Repulsive Deep Ensemble (FoRDE), an ensemble learning method based on ParVI, which performs repulsion in the space of first-order input gradients. As input gradients uniquely characterize a function up to translation and are much smaller in dimension than the weights, this method guarantees that ensemble members are functionally different. Intuitively, diversifying the input gradients encourages each network to learn different features, which is expected to improve the robustness of an ensemble. Experiments on image classification datasets and transfer learning tasks show that FoRDE significantly outperforms the gold-standard DEs and other ensemble methods in accuracy and calibration under covariate shift due to input perturbations. | https://openreview.net/pdf/bea7988b0afcf3076721719ce7a16e7b479e4de8.pdf |
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction | https://openreview.net/forum?id=otHZ8JAIgh | https://openreview.net/forum?id=otHZ8JAIgh | Yilan Zhang,Yingxue Xu,Jianqi Chen,Fengying Xie,Hao Chen | ICLR 2024,Spotlight | Multimodal learning significantly benefits cancer survival prediction, especially the integration of pathological images and genomic data. Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and compact information: (1) An extensive amount of intra-modal task-unrelated information blurs discriminability, especially for gigapixel whole slide images (WSIs) with many patches in pathology and thousands of pathways in genomic data, leading to an "intra-modal redundancy" issue. (2) Duplicated information among modalities dominates the representation of multimodal data, which makes modality-specific information prone to being ignored, resulting in an "inter-modal redundancy" issue. To address these, we propose a new framework, Prototypical Information Bottlenecking and Disentangling (PIBD), consisting of Prototypical Information Bottleneck (PIB) module for intra-modal redundancy and Prototypical Information Disentanglement (PID) module for inter-modal redundancy. Specifically, a variant of information bottleneck, PIB, is proposed to model prototypes approximating a bunch of instances for different risk levels, which can be used for selection of discriminative instances within modality. PID module decouples entangled multimodal data into compact distinct components: modality-common and modality-specific knowledge, under the guidance of the joint prototypical distribution. Extensive experiments on five cancer benchmark datasets demonstrated our superiority over other methods. The code is released. | https://openreview.net/pdf/3857619d5557d9869437c800e20c98370f49c9f8.pdf |
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data | https://openreview.net/forum?id=8xliOUg9EW | https://openreview.net/forum?id=8xliOUg9EW | Yinya Huang,Xiaohan Lin,Zhengying Liu,Qingxing Cao,Huajian Xin,Haiming Wang,Zhenguo Li,Linqi Song,Xiaodan Liang | ICLR 2024,Spotlight | Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict and formal multi-step inference, they are appealing domains for exploring the reasoning ability of LLMs but still face important challenges. Previous studies such as Chain-of-Thought (CoT) have revealed the effectiveness of intermediate steps guidance. However, such step-wise annotation requires heavy labor, leading to insufficient training steps for current benchmarks. To fill this gap, this work introduces MUSTARD, a data generation framework that masters uniform synthesis of theorem and proof data of high quality and diversity. MUSTARD synthesizes data in three stages: (1) It samples a few mathematical concept seeds as the problem category. (2) Then, it prompts a generative language model with the sampled concepts to obtain both the problems and their step-wise formal solutions. (3) Lastly, the framework utilizes a proof assistant (e.g., Lean Prover) to filter the valid proofs. With the proposed MUSTARD, we present a theorem-and-proof benchmark MUSTARDSAUCE with 5,866 valid data points. Each data point contains an informal statement, an informal proof, and a translated formal proof that passes the prover validation. We perform extensive analysis and demonstrate that MUSTARD generates validated high-quality step-by-step data. We further apply the MUSTARDSAUCE for fine-tuning smaller language models. The fine-tuned Llama 2-7B achieves a 15.41% average relative performance gain in automated theorem proving, and 8.18% in math word problems. Codes and data are available at https://github.com/Eleanor-H/MUSTARD. | https://openreview.net/pdf/426fb3de115df70dc1a81aa777b86b7e5afb7a75.pdf |
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning | https://openreview.net/forum?id=xsd2llWYSA | https://openreview.net/forum?id=xsd2llWYSA | Chenhao Li,Elijah Stanger-Jones,Steve Heim,Sang bae Kim | ICLR 2024,Spotlight | Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage. To address this challenge, we introduce a self-supervised, structured representation and generation method that extracts spatial-temporal relationships in periodic or quasi-periodic motions. The motion dynamics in a continuously parameterized latent space enable our method to enhance the interpolation and generalization capabilities of motion learning algorithms. The motion learning controller, informed by the motion parameterization, operates online tracking of a wide range of motions, including targets unseen during training. With a fallback mechanism, the controller dynamically adapts its tracking strategy and automatically resorts to safe action execution when a potentially risky target is proposed. By leveraging the identified spatial-temporal structure, our work opens new possibilities for future advancements in general motion representation and learning algorithms. | https://openreview.net/pdf/85c1068390ec60fc360e6c2d790602c02f0502ed.pdf |
Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features | https://openreview.net/forum?id=Tw9wemV6cb | https://openreview.net/forum?id=Tw9wemV6cb | Xiong Xu,Kunzhe Huang,Yiming Li,Zhan Qin,Kui Ren | ICLR 2024,Spotlight | Recent studies revealed that using third-party models may lead to backdoor threats, where adversaries can maliciously manipulate model predictions based on backdoors implanted during model training. Arguably, backdoor trigger inversion (BTI), which generates trigger patterns of given benign samples for a backdoored model, is the most critical module for backdoor defenses used in these scenarios. With BTI, defenders can remove backdoors by fine-tuning based on generated poisoned samples with ground-truth labels or deactivate backdoors by removing trigger patterns during the inference process. However, we find that existing BTI methods suffer from relatively poor performance, $i.e.$, their generated triggers are significantly different from the ones used by the adversaries even in the feature space. We argue that it is mostly because existing methods require to 'extract' backdoor features at first, while this task is very difficult since defenders have no information ($e.g.$, trigger pattern or target label) about poisoned samples. In this paper, we explore BTI from another perspective where we decouple benign features instead of decoupling backdoor features directly. Specifically, our method consists of two main steps, including \textbf{(1)} decoupling benign features and \textbf{(2)} trigger inversion by minimizing the differences between benign samples and their generated poisoned version in decoupled benign features while maximizing the differences in remaining backdoor features. In particular, our method is more efficient since it doesn't need to `scan' all classes to speculate the target label, as required by existing BTI. We also exploit our BTI module to further design backdoor-removal and pre-processing-based defenses. Extensive experiments on benchmark datasets demonstrate that our defenses can reach state-of-the-art performances. | https://openreview.net/pdf/9b2bab46886305e469d757ab239833eacb477014.pdf |
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data | https://openreview.net/forum?id=ziDFH8TPPK | https://openreview.net/forum?id=ziDFH8TPPK | Young-Jae Park,Minseok Seo,Doyi Kim,Hyeri Kim,Sanghoon Choi,Beomkyu Choi,Jeongwon Ryu,Sohee Son,Hae-Gon Jeon,Yeji Choi | ICLR 2024,Spotlight | In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditional physics-based models, while comprehensive, are computationally intensive and rely heavily on the expertise of forecasters. Contemporary data-driven methods often rely on reanalysis data, which can be considered to be the closest to the true representation of weather conditions. However, reanalysis data is not produced in real-time and requires time for adjustment since prediction models are calibrated with observational data. This reanalysis data, such as ERA5, falls short in challenging real-world situations. Optimal preparedness necessitates predictions at least 72 hours in advance, beyond the capabilities of standard physics models. In response to these constraints, we present an approach that harnesses real-time Unified Model (UM) data, sidestepping the limitations of reanalysis data. Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models. In line with our efforts to mitigate adversities inflicted by \rthree{typhoons}, we release our preprocessed \textit{PHYSICS TRACK} dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM forecast data. | https://openreview.net/pdf/3f35815328b8f1ce4a016bc05e882d4f298a97f8.pdf |
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection | https://openreview.net/forum?id=8iTpB4RNvP | https://openreview.net/forum?id=8iTpB4RNvP | Jiawei Liang,Siyuan Liang,Aishan Liu,Xiaojun Jia,Junhao Kuang,Xiaochun Cao | ICLR 2024,Spotlight | The proliferation of face forgery techniques has raised significant concerns within society, thereby motivating the development of face forgery detection methods. These methods aim to distinguish forged faces from genuine ones and have proven effective in practical applications. However, this paper introduces a novel and previously unrecognized threat in face forgery detection scenarios caused by backdoor attack. By embedding backdoors into models and incorporating specific trigger patterns into the input, attackers can deceive detectors into producing erroneous predictions for forged faces. To achieve this goal, this paper proposes \emph{Poisoned Forgery Face} framework, which enables clean-label backdoor attacks on face forgery detectors. Our approach involves constructing a scalable trigger generator and utilizing a novel convolving process to generate translation-sensitive trigger patterns. Moreover, we employ a relative embedding method based on landmark-based regions to enhance the stealthiness of the poisoned samples. Consequently, detectors trained on our poisoned samples are embedded with backdoors. Notably, our approach surpasses SoTA backdoor baselines with a significant improvement in attack success rate (+16.39\% BD-AUC) and reduction in visibility (-12.65\% $L_\infty$). Furthermore, our attack exhibits promising performance against backdoor defenses. We anticipate that this paper will draw greater attention to the potential threats posed by backdoor attacks in face forgery detection scenarios. Our codes will be made available at \url{https://github.com/JWLiang007/PFF}. | https://openreview.net/pdf/d2be569831b6c78668eb40760d2fcdfeb7ea4d51.pdf |
Unified Human-Scene Interaction via Prompted Chain-of-Contacts | https://openreview.net/forum?id=1vCnDyQkjg | https://openreview.net/forum?id=1vCnDyQkjg | Zeqi Xiao,Tai Wang,Jingbo Wang,Jinkun Cao,Wenwei Zhang,Bo Dai,Dahua Lin,Jiangmiao Pang | ICLR 2024,Spotlight | Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and the development of a user-friendly interface, require further exploration before the practical application of HSI. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands. The framework defines interaction as ``Chain of Contacts (CoC)", representing steps involving human joint-object part pairs. This concept is inspired by the strong correlation between interaction types and corresponding contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To facilitate training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. | https://openreview.net/pdf/ac7284562ef813e689f348d02334547591546d9b.pdf |
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration | https://openreview.net/forum?id=G32oY4Vnm8 | https://openreview.net/forum?id=G32oY4Vnm8 | Hangting Ye,Wei Fan,Xiaozhuang Song,Shun Zheng,He Zhao,Dan dan Guo,Yi Chang | ICLR 2024,Spotlight | Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc.
With the recent success of deep learning, many tabular machine learning (ML) methods based on deep networks (e.g., Transformer, ResNet) have achieved competitive performance on tabular benchmarks. However, existing deep tabular ML methods suffer from the representation entanglement and localization, which largely hinders their prediction performance and leads to performance inconsistency on tabular tasks.
To overcome these problems, we explore a novel direction of applying prototype learning for tabular ML and propose a prototype-based tabular representation learning framework, PTaRL, for tabular prediction tasks. The core idea of PTaRL is to construct prototype-based projection space (P-Space) and learn the disentangled representation around global data prototypes. Specifically, PTaRL mainly involves two stages: (i) Prototype Generating, that constructs global prototypes as the basis vectors of P-Space for representation, and (ii) Prototype Projecting, that projects the data samples into P-Space and keeps the core global data information via Optimal Transport. Then, to further acquire the disentangled representations, we constrain PTaRL with two strategies: (i) to diversify the coordinates towards global prototypes of different representations within P-Space, we bring up a diversifying constraint for representation calibration; (ii) to avoid prototype entanglement in P-Space, we introduce a matrix orthogonalization constraint to ensure the independence of global prototypes.
Finally, we conduct extensive experiments in PTaRL coupled with state-of-the-art deep tabular ML models on various tabular benchmarks and the results have shown our consistent superiority. | https://openreview.net/pdf/015d2b21c36da1eda54d0672c954af346b0c8ae1.pdf |
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data | https://openreview.net/forum?id=4VIgNuQ1pY | https://openreview.net/forum?id=4VIgNuQ1pY | YongKyung Oh,Dongyoung Lim,Sungil Kim | ICLR 2024,Spotlight | Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an alternative approach, utilizing neural networks combined with ODE solvers to learn continuous latent representations through parameterized vector fields. Neural Stochastic Differential Equations (Neural SDEs) extend Neural ODEs by incorporating a diffusion term, although this addition is not trivial, particularly when addressing irregular intervals and missing values. Consequently, careful design of drift and diffusion functions is crucial for maintaining stability and enhancing performance, while incautious choices can result in adverse properties such as the absence of strong solutions, stochastic destabilization, or unstable Euler discretizations, significantly affecting Neural SDEs' performance. In this study, we propose three stable classes of Neural SDEs: Langevin-type SDE, Linear Noise SDE, and Geometric SDE. Then, we rigorously demonstrate their robustness in maintaining excellent performance under distribution shift, while effectively preventing overfitting. To assess the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets for interpolation, forecasting, and classification tasks, and analyze the robustness of our methods with 30 public datasets under different missing rates. Our results demonstrate the efficacy of the proposed method in handling real-world irregular time series data. | https://openreview.net/pdf/999f7149f0b997016d06c2ea29b7f3da17d2ebc1.pdf |
What does the Knowledge Neuron Thesis Have to do with Knowledge? | https://openreview.net/forum?id=2HJRwwbV3G | https://openreview.net/forum?id=2HJRwwbV3G | Jingcheng Niu,Andrew Liu,Zining Zhu,Gerald Penn | ICLR 2024,Spotlight | We reassess the Knowledge Neuron (KN) Thesis: an interpretation of the mechanism underlying the ability of large language models to recall facts from a training corpus. This nascent thesis proposes that facts are recalled from the training corpus through the MLP weights in a manner resembling key-value memory, implying in effect that "knowledge" is stored in the network. Furthermore, by modifying the MLP modules, one can control the language model's generation of factual information. The plausibility of the KN thesis has been demonstrated by the success of KN-inspired model editing methods (Dai et al., 2022; Meng et al., 2022).
We find that this thesis is, at best, an oversimplification. Not only have we found that we can edit the expression of certain linguistic phenomena using the same model editing methods but, through a more comprehensive evaluation, we have found that the KN thesis does not adequately explain the process of factual expression. While it is possible to argue that the MLP weights store complex patterns that are interpretable both syntactically and semantically, these patterns do not constitute "knowledge." To gain a more comprehensive understanding of the knowledge representation process, we must look beyond the MLP weights and explore recent models' complex layer structures and attention mechanisms. | https://openreview.net/pdf/04389528ee4dfce215a52f1dd0987190ad2adc1d.pdf |
Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds | https://openreview.net/forum?id=DqziS8DG4M | https://openreview.net/forum?id=DqziS8DG4M | Jadie Adams,Shireen Elhabian | ICLR 2024,Spotlight | We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional methods of SSM construction have limitations, including the requirement of noise-free surface meshes or binary volumes, reliance on assumptions or templates, and prolonged inference times due to simultaneous optimization of the entire cohort. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. While deep learning on 3D point clouds has seen success in unsupervised representation learning and shape correspondence, its application to anatomical SSM construction is largely unexplored. We conduct a benchmark of state-of-the-art point cloud deep networks on the SSM task, revealing their limited robustness to clinical challenges such as noisy, sparse, or incomplete input and limited training data. Point2SSM addresses these issues through an attention-based module, providing effective correspondence mappings from learned point features. Our results demonstrate that the proposed method significantly outperforms existing networks in terms of accurate surface sampling and correspondence, better capturing population-level statistics. The source code is provided at https://github.com/jadie1/Point2SSM. | https://openreview.net/pdf/623e642ed0d9f91c3050e148c6b3074974e0fae4.pdf |
Improving Domain Generalization with Domain Relations | https://openreview.net/forum?id=Dc4rXq3HIA | https://openreview.net/forum?id=Dc4rXq3HIA | Huaxiu Yao,Xinyu Yang,Xinyi Pan,Shengchao Liu,Pang Wei Koh,Chelsea Finn | ICLR 2024,Spotlight | Distribution shift presents a significant challenge in machine learning, where models often underperform during the test stage when faced with a different distribution than the one they were trained on. In this paper, we focus on domain shifts, which occur when the model is applied to new domains that are different from the ones it was trained on, and propose a new approach called DG. Unlike previous approaches that aim to learn a single model that is domain invariant, DG leverages domain similarities based on domain metadata to learn domain-specific models. Concretely, DG learns a set of training-domain-specific functions during the training stage and reweights them based on domain relations during the test stage. These domain relations can be directly obtained and learned from domain metadata. Under mild assumptions, we theoretically prove that using domain relations to reweight training-domain-specific functions achieves stronger out-of-domain generalization compared to the conventional averaging approach. Empirically, we evaluate the effectiveness of DG using both toy and real-world datasets for tasks such as temperature regression, land use classification, and molecule-protein binding affinity prediction. Our results show that DG consistently outperforms state-of-the-art methods. | https://openreview.net/pdf/23ba11d88b46af7cce33aab0eac91aa8bb30ff30.pdf |
Generating Images with 3D Annotations Using Diffusion Models | https://openreview.net/forum?id=XlkN11Xj6J | https://openreview.net/forum?id=XlkN11Xj6J | Wufei Ma,Qihao Liu,Jiahao Wang,Angtian Wang,Xiaoding Yuan,Yi Zhang,Zihao Xiao,Guofeng Zhang,Beijia Lu,Ruxiao Duan,Yongrui Qi,Adam Kortylewski,Yaoyao Liu,Alan Yuille | ICLR 2024,Spotlight | Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure in the generated images. Consequently, this hinders our ability to obtain detailed 3D annotations for the generated images or to craft instances with specific poses and distances. In this paper, we propose 3D Diffusion Style Transfer (3D-DST), which incorporates 3D geometry control into diffusion models. Our method exploits ControlNet, which extends diffusion models by using visual prompts in addition to text prompts. We generate images of the 3D objects taken from 3D shape repositories~(e.g., ShapeNet and Objaverse), render them from a variety of poses and viewing directions, compute the edge maps of the rendered images, and use these edge maps as visual prompts to generate realistic images. With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically. This allows us to improve a wide range of vision tasks, e.g., classification and 3D pose estimation, in both in-distribution (ID) and out-of-distribution (OOD) settings. We demonstrate the effectiveness of our method through extensive experiments on ImageNet-100/200, ImageNet-R, PASCAL3D+, ObjectNet3D, and OOD-CV. The results show that our method significantly outperforms existing methods, e.g., 3.8 percentage points on ImageNet-100 using DeiT-B. Our code is available at <https://ccvl.jhu.edu/3D-DST/> | https://openreview.net/pdf/c5afc0f3ba7f4ccee44efa2439b9ebf433502898.pdf |
High-dimensional SGD aligns with emerging outlier eigenspaces | https://openreview.net/forum?id=MHjigVnI04 | https://openreview.net/forum?id=MHjigVnI04 | Gerard Ben Arous,Reza Gheissari,Jiaoyang Huang,Aukosh Jagannath | ICLR 2024,Spotlight | We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, the SGD trajectory rapidly aligns with emerging low-rank outlier eigenspaces of the Hessian and gradient matrices. Moreover, in multi-layer settings this alignment occurs per layer, with the final layer's outlier eigenspace evolving over the course of training, and exhibiting rank deficiency when the SGD converges to sub-optimal classifiers. This establishes some of the rich predictions that have arisen from extensive numerical studies in the last decade about the spectra of Hessian and information matrices over the course of training in overparametrized networks. | https://openreview.net/pdf/c8dc4127b70cc66dd5a649a848ee32a1b553666a.pdf |
$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis | https://openreview.net/forum?id=fLf589bx1f | https://openreview.net/forum?id=fLf589bx1f | Zishun Yu,Yunzhe Tao,Liyu Chen,Tao Sun,Hongxia Yang | ICLR 2024,Spotlight | Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context.
Recent studies have leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code generation capabilities. The application of RL focuses on directly optimizing for functional correctness, offering an advantage over conventional supervised methods.
Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods.
This stems from the rich collection of off-policy programs, including those developed by human programmers and also historical samples, coupled with the straightforward verification of generated programs through automated unit testing, meaning rewards are easy to obtain.
Diverging from the dominant use of policy-based algorithms, our work explores the feasibility of value-based approaches, leading to the development of our $\mathcal{B}$-Coder (pronounced Bellman coder).
Yet, training value-based methods presents challenges due to the enormous search space inherent to program synthesis.
To this end, we introduce an initialization protocol for RL agents utilizing pre-trained LMs and a conservative Bellman operator to reduce training complexities.
Moreover, we demonstrate how to leverage the learned value functions as a dual strategy to post-process generated programs.
Our empirical evaluations demonstrated $\mathcal{B}$-Coder's capability in achieving state-of-the-art performance when compared to policy-based methods.
Remarkably, this achievement is reached with minimal reward engineering effort, highlighting the effectiveness of value-based RL, independent of reward designs. | https://openreview.net/pdf/9d267a827e269389f34c62cd790e14e20c1e8a72.pdf |
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts | https://openreview.net/forum?id=auKAUJZMO6 | https://openreview.net/forum?id=auKAUJZMO6 | Jian Xie,Kai Zhang,Jiangjie Chen,Renze Lou,Yu Su | ICLR 2024,Spotlight | By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
However, how receptive are LLMs to such external evidence, especially when the evidence conflicts with their parametric memory?
We present the first comprehensive and controlled investigation into the behavior of LLMs when encountering knowledge conflicts.
We propose a systematic framework to elicit high-quality parametric memory from LLMs and construct the corresponding counter-memory, which enables us to conduct a series of controlled experiments.
Our investigation reveals seemingly contradicting behaviors of LLMs.
On the one hand, different from prior wisdom, we find that LLMs can be highly receptive to external evidence even when that conflicts with their parametric memory, given that the external evidence is coherent and convincing.
On the other hand, LLMs also demonstrate a strong confirmation bias when the external evidence contains some information that is consistent with their parametric memory, despite being presented with conflicting evidence at the same time.
These results pose important implications that are worth careful consideration for the further development and deployment of tool- and retrieval-augmented LLMs.
Resources are available at https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict. | https://openreview.net/pdf/89c45c674a8193212ede350c2f72b29b272db207.pdf |
A Hierarchical Bayesian Model for Few-Shot Meta Learning | https://openreview.net/forum?id=mQ72XRfYRZ | https://openreview.net/forum?id=mQ72XRfYRZ | Minyoung Kim,Timothy Hospedales | ICLR 2024,Spotlight | We propose a novel hierarchical Bayesian model for the few-shot meta learning problem. We consider episode-wise random variables to model episode-specific generative processes, where these local random variables are governed by a higher-level global random variable. The global variable captures information shared across episodes, while controlling how much the model needs to be adapted to new episodes in a principled Bayesian manner. Within our framework, prediction on a novel episode/task can be seen as a Bayesian inference problem. For tractable training, we need to be able to relate each local episode-specific solution to the global higher-level parameters. We propose a Normal-Inverse-Wishart model, for which establishing this local-global relationship becomes feasible due to the approximate closed-form solutions for the local posterior distributions. The resulting algorithm is more attractive than the MAML in that it does not maintain a costly computational graph for the sequence of gradient descent steps in an episode. Our approach is also different from existing Bayesian meta learning methods in that rather than modeling a single random variable for all episodes, it leverages a hierarchical structure that exploits the local-global relationships desirable for principled Bayesian learning with many related tasks. | https://openreview.net/pdf/92f087e2b731b4e0629d1db29e70e1a90a9d076f.pdf |
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models | https://openreview.net/forum?id=yKksu38BpM | https://openreview.net/forum?id=yKksu38BpM | Andrew William Engel,Zhichao Wang,Natalie Frank,Ioana Dumitriu,Sutanay Choudhury,Anand Sarwate,Tony Chiang | ICLR 2024,Spotlight | A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks. In this work, we combine these two trends to analyze approximate empirical neural tangent kernels (eNTK) for data attribution. Approximation is critical for eNTK analysis due to the high computational cost to compute the eNTK. We define new approximate eNTK and perform novel analysis on how well the resulting kernel machine surrogate models correlate with the underlying neural network. We introduce two new random projection variants of approximate eNTK which allow users to tune the time and memory complexity of their calculation. We conclude that kernel machines using approximate neural tangent kernel as the kernel function are effective surrogate models, with the introduced trace NTK the most consistent performer. | https://openreview.net/pdf/8cda600c2a15702e918b77654b5d7db852c98879.pdf |
Conformal Risk Control | https://openreview.net/forum?id=33XGfHLtZg | https://openreview.net/forum?id=33XGfHLtZg | Anastasios Nikolas Angelopoulos,Stephen Bates,Adam Fisch,Lihua Lei,Tal Schuster | ICLR 2024,Spotlight | We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee. Like conformal prediction, the conformal risk control procedure is tight up to an $\mathcal{O}(1/n)$ factor. We also introduce extensions of the idea to distribution shift, quantile risk control, multiple and adversarial risk control, and expectations of U-statistics. Worked examples from computer vision and natural language processing demonstrate the usage of our algorithm to bound the false negative rate, graph distance, and token-level F1-score. | https://openreview.net/pdf/482dcb57a6da0d60fce0a3b533b80bcbe99b03f6.pdf |
RetroBridge: Modeling Retrosynthesis with Markov Bridges | https://openreview.net/forum?id=770DetV8He | https://openreview.net/forum?id=770DetV8He | Ilia Igashov,Arne Schneuing,Marwin Segler,Michael M. Bronstein,Bruno Correia | ICLR 2024,Spotlight | Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing multi-step reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confidence estimates to guide heuristic search algorithms. We model single-step retrosynthesis as a distribution learning problem in a discrete state space. First, we introduce the Markov Bridge Model, a generative framework aimed to approximate the dependency between two intractable discrete distributions accessible via a finite sample of coupled data points. Our framework is based on the concept of a Markov bridge, a Markov process pinned at its endpoints. Unlike diffusion-based methods, our Markov Bridge Model does not need a tractable noise distribution as a sampling proxy and directly operates on the input product molecules as samples from the intractable prior distribution. We then address the retrosynthesis planning problem with our novel framework and introduce RetroBridge, a template-free retrosynthesis modeling approach that achieves state-of-the-art results on standard evaluation benchmarks. | https://openreview.net/pdf/20efd425e667325385a150734d8f20307ab8ccd4.pdf |
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior | https://openreview.net/forum?id=LtuRgL03pI | https://openreview.net/forum?id=LtuRgL03pI | Chenguo Lin,Yadong MU | ICLR 2024,Spotlight | Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the controllability of generation. We introduce InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis. The proposed semantic graph prior jointly learns scene appearances and layout distributions, exhibiting versatility across various downstream tasks in a zero-shot manner. To facilitate the benchmarking for text-driven 3D scene synthesis, we curate a high-quality dataset of scene-instruction pairs with large language and multimodal models. Extensive experimental results reveal that the proposed method surpasses existing state-of-the-art approaches by a large margin. Thorough ablation studies confirm the efficacy of crucial design components. Project page: https://chenguolin.github.io/projects/InstructScene. | https://openreview.net/pdf/c50a222a8cd87a10d51df5f3c0245a363e6cf567.pdf |
Single Motion Diffusion | https://openreview.net/forum?id=DrhZneqz4n | https://openreview.net/forum?id=DrhZneqz4n | Sigal Raab,Inbal Leibovitch,Guy Tevet,Moab Arar,Amit Haim Bermano,Daniel Cohen-Or | ICLR 2024,Spotlight | Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging domain, which is rich with large available datasets, the number of data instances for the motion domain is limited, particularly for the animation of animals and exotic creatures (e.g., dragons), which have unique skeletons and motion patterns. In this work, we introduce SinMDM, a Single Motion Diffusion Model. It is designed to learn the internal motifs of a single motion sequence with arbitrary topology and synthesize a variety of motions of arbitrary length that remain faithful to the learned motifs. We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion. SinMDM is crafted as a lightweight architecture, which avoids overfitting by using a shallow network with local attention layers that narrow the receptive field and encourage motion diversity. Our work applies to multiple contexts, including spatial and temporal in-betweening, motion expansion, style transfer, and crowd animation. Our results show that SinMDM outperforms existing methods both qualitatively and quantitatively. Moreover, while prior network-based approaches require additional training for different applications, SinMDM supports these applications during inference. Our project page, which includes links to the code and trained models, is accessible at https://sinmdm.github.io/SinMDM-page. | https://openreview.net/pdf/e0ae7f79fa711d623931b8987da94361b315e566.pdf |
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings | https://openreview.net/forum?id=5Dwqu5urzs | https://openreview.net/forum?id=5Dwqu5urzs | Hongpeng Cao,Yanbing Mao,Lui Sha,Marco Caccamo | ICLR 2024,Spotlight | This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invariant-embedding designs: i) residual action policy (i.e., integrating data-driven-DRL action policy and physics-model-based action policy), ii) automatically constructed safety-embedded reward, and iii) physics-model-guided neural network (NN) editing, including link editing and activation editing. Theoretically, the Phy-DRL exhibits 1) a mathematically provable safety guarantee and 2) strict compliance of critic and actor networks with physics knowledge about the action-value function and action policy. Finally, we evaluate the Phy-DRL on a cart-pole system and a quadruped robot. The experiments validate our theoretical results and demonstrate that Phy-DRL features guaranteed safety compared to purely data-driven DRL and solely model-based design while offering remarkably fewer learning parameters and fast training towards safety guarantee. | https://openreview.net/pdf/da97cca0d38172e4f655bd23891b3c1509513545.pdf |
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation | https://openreview.net/forum?id=sxGugrYhP9 | https://openreview.net/forum?id=sxGugrYhP9 | Han Zhang,Xiaofan Gui,Shun Zheng,Ziheng Lu,Yuqi Li,Jiang Bian | ICLR 2024,Spotlight | Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this intersection of electrochemical science and machine learning poses complex challenges. Machine learning experts often grapple with the intricacies of battery science, while battery researchers face hurdles in adapting intricate models tailored to specific datasets. Beyond this, a cohesive standard for battery degradation modeling, inclusive of data formats and evaluative benchmarks, is conspicuously absent. Recognizing these impediments, we present BatteryML—a one-step, all-encompass, and open-source platform designed to unify data preprocessing, feature extraction, and the implementation of both traditional and state-of-the-art models. This streamlined approach promises to enhance the practicality and efficiency of research applications. BatteryML seeks to fill this void, fostering an environment where experts from diverse specializations can collaboratively contribute, thus elevating the collective understanding and advancement of battery research. | https://openreview.net/pdf/513de629c33aecbc871ce161ed98f8f39a4662e9.pdf |
SaProt: Protein Language Modeling with Structure-aware Vocabulary | https://openreview.net/forum?id=6MRm3G4NiU | https://openreview.net/forum?id=6MRm3G4NiU | Jin Su,Chenchen Han,Yuyang Zhou,Junjie Shan,Xibin Zhou,Fajie Yuan | ICLR 2024,Spotlight | Large-scale protein language models (PLMs), such as the ESM family, have achieved remarkable performance in various downstream tasks related to protein structure and function by undergoing unsupervised training on residue sequences. They have become essential tools for researchers and practitioners in biology. However, a limitation of vanilla PLMs is their lack of explicit consideration for protein structure information, which suggests the potential for further improvement. Motivated by this, we introduce the concept of a ``structure-aware vocabulary" that integrates residue tokens with structure tokens. The structure tokens are derived by encoding the 3D structure of proteins using Foldseek. We then propose SaProt, a large-scale general-purpose PLM trained on an extensive dataset comprising approximately 40 million protein sequences and structures. Through extensive evaluation, our SaProt model surpasses well-established and renowned baselines across 10 significant downstream tasks, demonstrating its exceptional capacity and broad applicability. We have made the code, pre-trained model, and all relevant materials available at https://github.com/westlake-repl/SaProt. | https://openreview.net/pdf/5fccd3773c44a0809138e72b95c259790551303e.pdf |
PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis | https://openreview.net/forum?id=eAKmQPe3m1 | https://openreview.net/forum?id=eAKmQPe3m1 | Junsong Chen,Jincheng YU,Chongjian GE,Lewei Yao,Enze Xie,Zhongdao Wang,James Kwok,Ping Luo,Huchuan Lu,Zhenguo Li | ICLR 2024,Spotlight | The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PixArt-$\alpha$, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PixArt-$\alpha$'s training speed markedly surpasses existing large-scale T2I models, e.g., PixArt-$\alpha$ only takes 10.8% of Stable Diffusion v1.5's training time (~675 vs. ~6,250 A100 GPU days), saving nearly \\$300,000 (\\$26,000 vs. \\$320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PixArt-$\alpha$ excels in image quality, artistry, and semantic control. We hope PixArt-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch. | https://openreview.net/pdf/1b5209278e0321ca4b148bb7d58459bd314e8e2c.pdf |
Sentence-level Prompts Benefit Composed Image Retrieval | https://openreview.net/forum?id=m3ch3kJL7q | https://openreview.net/forum?id=m3ch3kJL7q | Yang bai,Xinxing Xu,Yong Liu,Salman Khan,Fahad Khan,Wangmeng Zuo,Rick Siow Mong Goh,Chun-Mei Feng | ICLR 2024,Spotlight | Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adopt the late-fusion strategy to combine visual and language features. Besides, several approaches have also been suggested to generate a pseudo-word token from the reference image, which is further integrated into the relative caption for CIR. However, these pseudo-word-based prompting methods have limitations when target image encompasses complex changes on reference image, e.g., object removal and attribute modification. In this work, we demonstrate that learning an appropriate sentence-level prompt for the relative caption (SPRC) is sufficient for achieving effective composed image retrieval. Instead of relying on pseudo- word-based prompts, we propose to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence-level prompts. By concatenating the learned sentence-level prompt with the relative caption, one can readily use existing text-based image retrieval models to enhance CIR performance. Furthermore, we introduce both image-text contrastive loss and text prompt alignment loss to enforce the learning of suitable sentence-level prompts. Experiments show that our proposed method performs favorably against the state-of-the-art CIR methods on the Fashion-IQ and CIRR datasets. | https://openreview.net/pdf/440012f67fd4222bd4ed875af49d99018d3f2b8c.pdf |
Compositional Generative Inverse Design | https://openreview.net/forum?id=wmX0CqFSd7 | https://openreview.net/forum?id=wmX0CqFSd7 | Tailin Wu,Takashi Maruyama,Long Wei,Tao Zhang,Yilun Du,Gianluca Iaccarino,Jure Leskovec | ICLR 2024,Spotlight | Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical engineering to aerospace engineering. Inverse design is typically formulated as an optimization problem, with recent works leveraging optimization across learned dynamics models. However, as models are optimized they tend to fall into adversarial modes, preventing effective sampling. We illustrate that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples and significantly improve design performance. We further illustrate how such a design system is compositional, enabling us to combine multiple different diffusion models representing subcomponents of our desired system to design systems with every specified component. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes that are more complex than those in the training data. Our method generalizes to more objects for N-body dataset and discovers formation flying to minimize drag in the multi-airfoil design task. Project website and code can be found at https://github.com/AI4Science-WestlakeU/cindm. | https://openreview.net/pdf/ea28dd2d95cfceacb17820ff058ae38eec039540.pdf |
What does automatic differentiation compute for neural networks? | https://openreview.net/forum?id=8vKknbgXxf | https://openreview.net/forum?id=8vKknbgXxf | Sejun Park,Sanghyuk Chun,Wonyeol Lee | ICLR 2024,Spotlight | Forward- or reverse-mode automatic differentiation (AD) is a popular algorithm for computing the derivative of a function expressed by a program. AD always outputs the correct derivative if a program does not use any non-differentiable functions and control flows; however, it may return an arbitrary value otherwise. In this work, we investigate what AD computes for neural networks that may contain non-differentiable functions such as ReLU and maxpools. We first prove that AD always returns a generalized derivative called a Clarke subderivative for networks with pointwise activation functions, if the minibatch size is one and all non-differentiable neurons have distinct bias parameters. We show that the same conclusion does not hold otherwise, but does hold under some mild sufficient conditions. We also prove similar results for more general networks that can use maxpools and bias parameters shared across different neurons. We empirically check our sufficient conditions over popular network architectures and observe that AD almost always computes a Clarke subderivative in practical learning setups. | https://openreview.net/pdf/5dd1c7a6e5c5d0b42966e7e1500d634ee275c07b.pdf |
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models | https://openreview.net/forum?id=8Wuvhh0LYW | https://openreview.net/forum?id=8Wuvhh0LYW | Wenqi Shao,Mengzhao Chen,Zhaoyang Zhang,Peng Xu,Lirui Zhao,Zhiqian Li,Kaipeng Zhang,Peng Gao,Yu Qiao,Ping Luo | ICLR 2024,Spotlight | Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, leading to low performance, especially in extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization ($\textbf{OmniQuant}$) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family size 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4 (4-bit weight, 4-bit activation), W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes are available at
\url{https://github.com/OpenGVLab/OmniQuant}. | https://openreview.net/pdf/3eb6251031dde954f0cded606252c2de4b922e4d.pdf |
Ferret: Refer and Ground Anything Anywhere at Any Granularity | https://openreview.net/forum?id=2msbbX3ydD | https://openreview.net/forum?id=2msbbX3ydD | Haoxuan You,Haotian Zhang,Zhe Gan,Xianzhi Du,Bowen Zhang,Zirui Wang,Liangliang Cao,Shih-Fu Chang,Yinfei Yang | ICLR 2024,Spotlight | We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding open-vocabulary descriptions. To unify referring and grounding in the LLM paradigm, Ferret employs a novel and powerful hybrid region representation that integrates discrete coordinates and continuous features jointly to represent a region in the image. To extract the continuous features of versatile regions, we propose a spatial-aware visual sampler, adept at handling varying sparsity across different shapes. Consequently, Ferret can accept diverse region inputs, such as points, bounding boxes, and free-form shapes. To bolster the desired capability of Ferret, we curate GRIT, a comprehensive refer-and-ground instruction tuning dataset including 1.1M samples that contain rich hierarchical spatial knowledge, with an additional 130K hard negative data to promote model robustness. The resulting model not only achieves superior performance in classical referring and grounding tasks, but also greatly outperforms existing MLLMs in region-based and localization-demanded multimodal chatting. Our evaluations also reveal a significantly improved capability of describing image details and a remarkable alleviation in object hallucination. | https://openreview.net/pdf/2334b28229d561c4b8239640b86f836796cdfa2f.pdf |
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation | https://openreview.net/forum?id=gn0mIhQGNM | https://openreview.net/forum?id=gn0mIhQGNM | Chongyu Fan,Jiancheng Liu,Yihua Zhang,Eric Wong,Dennis Wei,Sijia Liu | ICLR 2024,Spotlight | With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often suffer limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' for MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting data points). To the best of our knowledge, SalUn is the first principled MU approach that can effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation tasks. As highlighted below, For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not. Codes are available at https://github.com/OPTML-Group/Unlearn-Saliency.
**WARNING**: This paper contains model outputs that may be offensive in nature. | https://openreview.net/pdf/d1a26ff55c8290e1b5ca6daf635489a64bba1162.pdf |
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization | https://openreview.net/forum?id=KOZu91CzbK | https://openreview.net/forum?id=KOZu91CzbK | Weiran Yao,Shelby Heinecke,Juan Carlos Niebles,Zhiwei Liu,Yihao Feng,Le Xue,Rithesh R N,Zeyuan Chen,Jianguo Zhang,Devansh Arpit,Ran Xu,Phil L Mui,Huan Wang,Caiming Xiong,Silvio Savarese | ICLR 2024,Spotlight | Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however, are not optimized using environment-specific rewards. Although some agents enable iterative refinement through verbal feedback, they do not reason and plan in ways that are compatible with gradient-based learning from rewards. This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model, which automatically tunes the language agent prompts from environment feedback through policy gradient. Specifically, our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model which refines the language agent prompt by summarizing the root cause of prior failed attempts and proposing action plans. Experimental results on various tasks demonstrate that the language agents improve over time and that our approach considerably outperforms baselines that do not properly leverage gradients from the environment. | https://openreview.net/pdf/d02f39256b41ec50ca8cd3a5b136065bc7a4caae.pdf |
BECLR: Batch Enhanced Contrastive Few-Shot Learning | https://openreview.net/forum?id=k9SVcrmXL8 | https://openreview.net/forum?id=k9SVcrmXL8 | Stylianos Poulakakis-Daktylidis,Hadi Jamali-Rad | ICLR 2024,Spotlight | Learning quickly from very few labeled samples is a fundamental attribute that separates machines and humans in the era of deep representation learning. Unsupervised few-shot learning (U-FSL) aspires to bridge this gap by discarding the reliance on annotations at training time. Intrigued by the success of contrastive learning approaches in the realm of U-FSL, we structurally approach their shortcomings in both pretraining and downstream inference stages. We propose a novel Dynamic Clustered mEmory (DyCE) module to promote a highly separable latent representation space for enhancing positive sampling at the pretraining phase and infusing implicit class-level insights into unsupervised contrastive learning. We then tackle the, somehow overlooked yet critical, issue of sample bias at the few-shot inference stage. We propose an iterative Optimal Transport-based distribution Alignment (OpTA) strategy and demonstrate that it efficiently addresses the problem, especially in low-shot scenarios where FSL approaches suffer the most from sample bias. We later on discuss that DyCE and OpTA are two intertwined pieces of a novel end-to-end approach (we coin as BECLR), constructively magnifying each other's impact. We then present a suite of extensive quantitative and qualitative experimentation to corroborate that BECLR sets a new state-of-the-art across ALL existing U-FSL benchmarks (to the best of our knowledge), and significantly outperforms the best of the current baselines (codebase available at https://github.com/stypoumic/BECLR). | https://openreview.net/pdf/4e0ce4a0dbf6fbf7d4ad5cc091ce7a0df0dc793b.pdf |
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation | https://openreview.net/forum?id=v0zNCwwkaV | https://openreview.net/forum?id=v0zNCwwkaV | Josh Alman,Zhao Song | ICLR 2024,Spotlight | In the classical transformer attention scheme, we are given three $n \times d$ size matrices $Q, K, V$ (the query, key, and value tokens), and the goal is to compute a new $n \times d$ size matrix $D^{-1} \exp(QK^\top) V$ where $D = \mathrm{diag}( \exp(QK^\top) {\bf 1}_n )$. Here, $\exp()$ is applied entry-wise and ${\bf 1}_n$ denotes a length-$n$ vector whose entries are all ones.
Intuitively, attention computation captures pairwise information between words in a sentence, but not higher-order information. Indeed, recent work \cite{sht23} has shown that attention units cannot solve simple problems about detecting triples of connected words.
In this work, we study a generalization of attention which captures triple-wise correlations. The generalization is based on computations involving tensors defined by tuples of words. More formally, given five $n \times d$ size matrices $Q, K_1, K_2, V_1$ and $V_2$ (generalized query, key, and value tokens), our new goal is to compute an $n \times d$ size matrix $D^{-1} \exp( Q ( K_1 \oslash K_2)^\top ) (V_1 \oslash V_2) $ where $D = \mathrm{diag}( \exp( Q ( K_1 \oslash K_2)^\top ) {\bf 1}_{n^2} )$ and $K_1 \oslash K_2 \in \mathbb{R}^{n^2 \times d}$ denotes the column-wise Kronecker product of $K_1$ and $K_2$. This generalization is indeed able to solve problems about detecting triple-wise connections that were shown to be impossible for transformers.
The potential downside of this generalization is that it appears as though computations are even more difficult, since the straightforward algorithm requires cubic time in $n$. However, we show that in the bounded-entry setting (which arises in practice, and which is well-studied in both theory and practice), there is actually a near-linear time algorithm. More precisely, we show that bounded entries are both necessary and sufficient for quickly performing generalized computations:
$\bullet$ On the positive side, if all entries of the input matrices are bounded above by $o(\sqrt[3]{\log n})$ then we show how to approximate the ``tensor-type'' attention matrix in $n^{1+o(1)}$ time.
$\bullet$ On the negative side, we show that if the entries of the input matrices may be as large as $\Omega(\sqrt[3]{\log n})$, then there is no algorithm that runs faster than $n^{3-o(1)}$ (assuming the Strong Exponential
Time Hypothesis from fine-grained complexity theory).
We also show that our construction, algorithms, and lower bounds naturally generalize to higher-order tensors and correlations. Interestingly, the higher the order of the tensors, the lower the bound on the entries needs to be for an efficient algorithm. Our results thus yield a natural tradeoff between the boundedness of the entries, and order of the tensor one may use for more expressive, efficient attention computation.
Our constructions make use of a novel connection with a higher-order variant on the kernel density estimation problem. They combine a number of technical tools, including the polynomial method, algebraic geometry codes, and multiparty Merlin-Arthur communication protocols. | https://openreview.net/pdf/c5ca1dc9f485832dce82dbf8d2569d405b9f37fb.pdf |
DreamLLM: Synergistic Multimodal Comprehension and Creation | https://openreview.net/forum?id=y01KGvd9Bw | https://openreview.net/forum?id=y01KGvd9Bw | Runpei Dong,Chunrui Han,Yuang Peng,Zekun Qi,Zheng Ge,Jinrong Yang,Liang Zhao,Jianjian Sun,Hongyu Zhou,Haoran Wei,Xiangwen Kong,Xiangyu Zhang,Kaisheng Ma,Li Yi | ICLR 2024,Spotlight | This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. Project page: https://dreamllm.github.io. | https://openreview.net/pdf/90127f90e1f27ec4c2fa4cd59e1921ab9f5d0a31.pdf |
Learning to Act from Actionless Videos through Dense Correspondences | https://openreview.net/forum?id=Mhb5fpA1T0 | https://openreview.net/forum?id=Mhb5fpA1T0 | Po-Chen Ko,Jiayuan Mao,Yilun Du,Shao-Hua Sun,Joshua B. Tenenbaum | ICLR 2024,Spotlight | In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations. Our method leverages images as a task-agnostic representation, encoding both the state and action information, and text as a general representation for specifying robot goals. By synthesizing videos that "hallucinate" robot executing actions and in combination with dense correspondences between frames, our approach can infer the closed-formed action to execute to an environment without the need of any explicit action labels. This unique capability allows us to train the policy solely based on RGB videos and deploy learned policies to various robotic tasks. We demonstrate the efficacy of our approach in learning policies on table-top manipulation and navigation tasks. Additionally, we contribute an open-source framework for efficient video modeling, enabling the training of high-fidelity policy models with four GPUs within a single day. | https://openreview.net/pdf/fff0f1d4e51a3d9d660e98a91daabc1b70364dbd.pdf |
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation | https://openreview.net/forum?id=CvYBvgEUK9 | https://openreview.net/forum?id=CvYBvgEUK9 | Jeongyeol Kwon,Dohyun Kwon,Stephen Wright,Robert D Nowak | ICLR 2024,Spotlight | In this work, we study first-order algorithms for solving Bilevel Optimization (BO) where the objective functions are smooth but possibly nonconvex in both levels and the variables are restricted to closed convex sets. As a first step, we study the landscape of BO through the lens of penalty methods, in which the upper- and lower-level objectives are combined in a weighted sum with penalty parameter $\sigma > 0$. In particular, we establish a strong connection between the penalty function and the hyper-objective by explicitly characterizing the conditions under which the values and derivatives of the two must be $O(\sigma)$-close. A by-product of our analysis is the explicit formula for the gradient of hyper-objective when the lower-level problem has multiple solutions under minimal conditions, which could be of independent interest. Next, viewing the penalty formulation as $O(\sigma)$-approximation of the original BO, we propose first-order algorithms that find an $\epsilon$-stationary solution by optimizing the penalty formulation with $\sigma = O(\epsilon)$. When the perturbed lower-level problem uniformly satisfies the {\it small-error} proximal error-bound (EB) condition, we propose a first-order algorithm that converges to an $\epsilon$-stationary point of the penalty function using in total $O(\epsilon^{-7})$ accesses to first-order stochastic gradient oracles. Under an additional assumption on stochastic oracles, we show that the algorithm can be implemented in a fully {\it single-loop} manner, {\it i.e.,} with $O(1)$ samples per iteration, and achieves the improved oracle-complexity of $O(\epsilon^{-5})$. | https://openreview.net/pdf/7e1da919067d21451ef10b9db63ead9823c7395f.pdf |
Scaling Laws for Sparsely-Connected Foundation Models | https://openreview.net/forum?id=i9K2ZWkYIP | https://openreview.net/forum?id=i9K2ZWkYIP | Elias Frantar,Carlos Riquelme Ruiz,Neil Houlsby,Dan Alistarh,Utku Evci | ICLR 2024,Spotlight | We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains. In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data, which we validate empirically across model and data scales; on ViT/JFT-4B and T5/C4. These results allow us to characterize the "optimal sparsity", the sparsity level which yields the best performance for a given effective model size and training budget. For a fixed number of non-zero parameters, we identify that the optimal sparsity increases with the amount of data used for training. We also extend our study to different sparsity structures (such as the hardware-friendly n:m pattern) and strategies (such as starting from a pretrained dense model). Our findings shed light on the power and limitations of weight sparsity across various parameter and computational settings, offering both theoretical understanding and practical implications for leveraging sparsity towards computational efficiency improvements. We provide pruning and scaling law fitting code at: github.com/google-research/jaxpruner/tree/main/jaxpruner/projects/bigsparse. | https://openreview.net/pdf/07a48d9ea45fa58df98ef989ab0046935ac248cf.pdf |
Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization | https://openreview.net/forum?id=r5njV3BsuD | https://openreview.net/forum?id=r5njV3BsuD | Joe Benton,Valentin De Bortoli,Arnaud Doucet,George Deligiannidis | ICLR 2024,Spotlight | Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions. Recent results provide polynomial bounds on their convergence rate, assuming $L^2$-accurate scores. Until now, the tightest bounds were either superlinear in the data dimension or required strong smoothness assumptions. We provide the first convergence bounds which are linear in the data dimension (up to logarithmic factors) assuming only finite second moments of the data distribution. We show that diffusion models require at most $\tilde O(\frac{d \log^2(1/\delta)}{\varepsilon^2})$ steps to approximate an arbitrary distribution on $\mathbb{R}^d$ corrupted with Gaussian noise of variance $\delta$ to within $\varepsilon^2$ in KL divergence. Our proof extends the Girsanov-based methods of previous works. We introduce a refined treatment of the error from discretizing the reverse SDE inspired by stochastic localization. | https://openreview.net/pdf/b3be4a4ee32009fa1997a1c54f00e4168ebc1980.pdf |
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models | https://openreview.net/forum?id=OEL4FJMg1b | https://openreview.net/forum?id=OEL4FJMg1b | Chong Mou,Xintao Wang,Jiechong Song,Ying Shan,Jian Zhang | ICLR 2024,Spotlight | Despite the ability of text-to-image (T2I) diffusion models to generate high-quality images, transferring this ability to accurate image editing remains a challenge. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we treat image editing as the change of feature correspondence in a pre-trained diffusion model. By leveraging feature correspondence, we develop energy functions that align with the editing target, transforming image editing operations into gradient guidance. Based on this guidance approach, we also construct multi-scale guidance that considers both semantic and geometric alignment. Furthermore, we incorporate a visual cross-attention strategy based on a memory bank design to ensure consistency between the edited result and original image. Benefiting from these efficient designs, all content editing and consistency operations come from the feature correspondence without extra model fine-tuning. Extensive experiments demonstrate that our method has promising performance on various image editing tasks, including within a single image (e.g., object moving, resizing, and content dragging) or across images (e.g., appearance replacing and object pasting). Code is available at https://github.com/MC-E/DragonDiffusion. | https://openreview.net/pdf/d1d88ddeb5844da8d6140ed188e076244748489e.pdf |
Uni3D: Exploring Unified 3D Representation at Scale | https://openreview.net/forum?id=wcaE4Dfgt8 | https://openreview.net/forum?id=wcaE4Dfgt8 | Junsheng Zhou,Jinsheng Wang,Baorui Ma,Yu-Shen Liu,Tiejun Huang,Xinlong Wang | ICLR 2024,Spotlight | Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes is relatively unexplored. In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. Via the simple architecture and pretext task, Uni3D can leverage abundant 2D pretrained models as initialization and image-text aligned models as the target, unlocking the great potential of 2D model zoos and scaling-up strategies to the 3D world. We efficiently scale up Uni3D to one billion parameters, and set new records on a broad range of 3D tasks, such as zero-shot classification, few-shot classification, open-world understanding and zero-shot part segmentation. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild. We believe that Uni3D provides a new direction for exploring both scaling up and efficiency of the representation in 3D domain. | https://openreview.net/pdf/88048b7b0b8f24d97f795d4218b905bbe24a2a9e.pdf |
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents | https://openreview.net/forum?id=UBVNwD3hPN | https://openreview.net/forum?id=UBVNwD3hPN | Siyuan Qi,Shuo Chen,Yexin Li,Xiangyu Kong,Junqi Wang,Bangcheng Yang,Pring Wong,Yifan Zhong,Xiaoyuan Zhang,Zhaowei Zhang,Nian Liu,Yaodong Yang,Song-Chun Zhu | ICLR 2024,Spotlight | The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in novel contexts. However, the predominant emphasis in most interactive environments is on learning, often at the expense of complexity in reasoning. In this paper, we introduce CivRealm, an environment inspired by the Civilization game. Civilization’s profound alignment with human society requires sophisticated learning and prior knowledge, while its ever-changing space and action space demand robust reasoning for generalization. Particularly, CivRealm sets up an imperfect-information general-sum game with a changing number of players; it presents a plethora of complex features, challenging the agent to deal with open-ended stochastic environments that require diplomacy and negotiation skills. Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning. To catalyze further research, we present initial results for both paradigms. The canonical RL-based agents exhibit reasonable performance in mini-games, whereas both RL- and LLM-based agents struggle to make substantial progress in the full game. Overall, CivRealm stands as a unique learning and reasoning challenge for decision-making agents. The code is available at https://github.com/bigai-ai/civrealm. | https://openreview.net/pdf/8e9b4884f752883a17e58942608d9cc769610a01.pdf |
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy | https://openreview.net/forum?id=EXitynZhYn | https://openreview.net/forum?id=EXitynZhYn | Simon Ging,Maria Alejandra Bravo,Thomas Brox | ICLR 2024,Spotlight | The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks and proposing innovative evaluation methodologies, our research seeks to advance our understanding of these models’ capabilities. We propose a novel VQA benchmark based on well-known visual classification datasets which allows a granular evaluation of text-generative vision-language models and their comparison with discriminative vision-language models. To improve the assessment of coarse answers on fine-grained classification tasks, we suggest using the semantic hierarchy of the label space to ask automatically generated follow-up questions about the ground-truth category. Finally, we compare traditional NLP and LLM-based metrics for the problem of evaluating model predictions given ground-truth answers. We perform a human evaluation study upon which we base our decision on the final metric. We apply our benchmark to a suite of vision-language models and show a detailed comparison of their abilities on object, action, and attribute classification. Our contributions aim to lay the foundation for more precise and meaningful assessments, facilitating targeted progress in the exciting field of vision-language modeling. | https://openreview.net/pdf/aa20ca1d2b8b77f839621a27a8e079ff29b639d0.pdf |
GIM: Learning Generalizable Image Matcher From Internet Videos | https://openreview.net/forum?id=NYN1b8GRGS | https://openreview.net/forum?id=NYN1b8GRGS | Xuelun Shen,zhipeng cai,Wei Yin,Matthias Müller,Zijun Li,Kaixuan Wang,Xiaozhi Chen,Cheng Wang | ICLR 2024,Spotlight | Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types (e.g., indoor vs. outdoor) and are impractical when the scene type is unknown in advance. One of the underlying problems is the limited scalability of existing data construction pipelines, which limits the diversity of standard image matching datasets. To address this problem, we propose GIM, a self-training framework for learning a single generalizable model based on any image matching architecture using internet videos, an abundant and diverse data source. Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos. These labels are filtered by robust fitting, and then enhanced by propagating them to distant frames. The final model is trained on propagated data with strong augmentations. Not relying on complex 3D reconstruction makes GIM much more efficient and less likely to fail than standard SfM-and-MVS based frameworks. We also propose ZEB, the first zero-shot evaluation benchmark for image matching. By mixing data from diverse domains, ZEB can thoroughly assess the cross-domain generalization performance of different methods. Experiments demonstrate the effectiveness and generality of GIM. Applying GIM consistently improves the zero-shot performance of 3 state-of-the-art image matching architectures as the number of downloaded videos increases (Fig. 1 (a)); with 50 hours of YouTube videos, the relative zero-shot performance improves by 6.9% − 18.1%. GIM also enables generalization to extreme cross-domain data such as Bird Eye View (BEV) images of projected 3D point clouds (Fig. 1 (c)). More importantly, our single zero-shot model consistently outperforms domain-specific baselines when evaluated on downstream tasks inherent to their respective domains. The code will be released upon acceptance. | https://openreview.net/pdf/c51ae05af771bb0017771606dc8f8cc514003720.pdf |
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image | https://openreview.net/forum?id=MN3yH2ovHb | https://openreview.net/forum?id=MN3yH2ovHb | Yuan Liu,Cheng Lin,Zijiao Zeng,Xiaoxiao Long,Lingjie Liu,Taku Komura,Wenping Wang | ICLR 2024,Spotlight | In this paper, we present a novel diffusion model called SyncDreamer that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D. Project page: https://liuyuan-pal.github.io/SyncDreamer/. | https://openreview.net/pdf/da8460ef21ce4f5cdf6d529397c3d451785545be.pdf |
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