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[] | Poster | [] | Are camera poses necessary for multi-view 3D modeling? Existing approaches predominantly assume access to accurate camera poses. While this assumption might hold for dense views, accurately estimating camera poses for sparse views is often elusive. Our analysis reveals that noisy estimated poses lead to degraded performance for existing sparse-view 3D modeling methods. To address this issue, we present LEAP, a novel pose-free approach, therefore challenging the prevailing notion that camera poses are indispensable. LEAP discards pose-based operations and learns geometric knowledge from data. LEAP is equipped with a neural volume, which is shared across scenes and is parameterized to encode geometry and texture priors. For each incoming scene, we update the neural volume by aggregating 2D image features in a feature-similarity-driven manner. The updated neural volume is decoded into the radiance field, enabling novel view synthesis from any viewpoint. On both object-centric and scene-level datasets, we show that LEAP significantly outperforms prior methods when they employ predicted poses from state-of-the-art pose estimators. Notably, LEAP performs on par with prior approaches that use ground-truth poses while running $400\times$ faster than PixelNeRF. We show LEAP generalizes to novel object categories and scenes, and learns knowledge closely resembles epipolar geometry. | [] | [] | LEAP: Liberate Sparse-View 3D Modeling from Camera Poses | [
"Hanwen Jiang",
"Zhenyu Jiang",
"Yue Zhao",
"Qixing Huang"
] | 2310.01410 | 18,907 | https://openreview.net/forum?id=KPmajBxEaF |
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[] | Poster | [] | Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a) covariate information through the constraint that instances with similar covariates should have similar labels and b) the bag level aggregated label. We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels. In the second step (Embedding Refinement), we use the pseudo labels to provide supervision for a learner that yields a better embedding. Further, we iterate on the two steps again by using the second step's embeddings as new covariates for the next iteration. In the final iteration, a classifier is trained using the pseudo labels. Our algorithm displays strong gains against several SOTA baselines (upto **15%**) for the LLP Binary Classification problem on various dataset types - tabular and Image. We achieve these improvements with minimal computational overhead above standard supervised learning due to Belief Propagation, for large bag sizes, even for a million samples. | [] | [] | Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation | [
"Shreyas Havaldar",
"Navodita Sharma",
"Shubhi Sareen",
"Karthikeyan Shanmugam",
"Aravindan Raghuveer"
] | 2310.08056 | 18,905 | https://openreview.net/forum?id=KQe9tHd0k8 |
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[] | Poster | [] | Self-supervised learning (SSL) has recently received significant attention due to its ability to train high-performance encoders purely on unlabeled data---often scraped from the internet. This data can still be sensitive and empirical evidence suggests that SSL encoders memorize private information of their training data and can disclose them at inference time. Since existing theoretical definitions of memorization from supervised learning rely on labels, they do not transfer to SSL. To address this gap, we propose a framework for defining memorization within the context of SSL. Our definition compares the difference in alignment of representations for data points and their augmented views returned by both encoders that were trained on these data points and encoders that were not. Through comprehensive empirical analysis on diverse encoder architectures and datasets we highlight that even though SSL relies on large datasets and strong augmentations---both known in supervised learning as regularization techniques that reduce overfitting---still significant fractions of training data points experience high memorization. Through our empirical results, we show that this memorization is essential for encoders to achieve higher generalization performance on different downstream tasks. | [] | [] | Memorization in Self-Supervised Learning Improves Downstream Generalization | [
"Wenhao Wang",
"Muhammad Ahmad Kaleem",
"Adam Dziedzic",
"Michael Backes",
"Nicolas Papernot",
"Franziska Boenisch"
] | 2401.12233 | 18,903 | https://openreview.net/forum?id=KSjPaXtxP8 |
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[] | Poster | [] | The reasoning capabilities of LLM (Large Language Model) are widely acknowledged in recent research, inspiring studies on tool learning and autonomous agents. LLM serves as the ``brain'' of agent, orchestrating multiple tools for collaborative multi-step task solving. Unlike methods invoking tools like calculators or weather APIs for straightforward tasks, multi-modal agents excel by integrating diverse AI models for complex challenges. However, current multi-modal agents neglect the significance of model selection: they primarily focus on the planning and execution phases, and will only invoke predefined task-specific models for each subtask, making the execution fragile. Meanwhile, other traditional model selection methods are either incompatible with or suboptimal for the multi-modal agent scenarios, due to ignorance of dependencies among subtasks arising by multi-step reasoning.To this end, we identify the key challenges therein and propose the $\textbf{\textit{M}}^\textbf{\textit{3}}$ framework as a plug-in with negligible runtime overhead at test-time. This framework improves model selection and bolsters the robustness of multi-modal agents in multi-step reasoning. In the absence of suitable benchmarks, we create MS-GQA, a new dataset specifically designed to investigate the model selection challenge in multi-modal agents. Our experiments reveal that our framework enables dynamic model selection, considering both user inputs and subtask dependencies, thereby robustifying the overall reasoning process. | [] | [] | Towards Robust Multi-Modal Reasoning via Model Selection | [
"Xiangyan Liu",
"Rongxue LI",
"Wei Ji",
"Tao Lin"
] | 2310.08446 | 18,902 | https://openreview.net/forum?id=KTf4DGAzus |
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[] | Poster | [] | Nonprehensile manipulation is essential for manipulating objects that are too thin, large, or otherwise ungraspable in the wild. To sidestep the difficulty of contact modeling in conventional modeling-based approaches, reinforcement learning (RL) has recently emerged as a promising alternative. However, previous RL approaches either lack the ability to generalize over diverse object shapes, or use simple action primitives that limit the diversity of robot motions. Furthermore, using RL over diverse object geometry is challenging due to the high cost of training a policy that takes in high-dimensional sensory inputs. We propose a novel contact-based object representation and pretraining pipeline to tackle this. To enable massively parallel training, we leverage a lightweight patch-based transformer architecture for our encoder that processes point clouds, thus scaling our training across thousands of environments. Compared to learning from scratch, or other shape representation baselines, our representation facilitates both time- and data-efficient learning. We validate the efficacy of our overall system by zero-shot transferring the trained policy to novel real-world objects. We highly recommend the video attached in the supplementary material. Code and videos are available at \url{https://sites.google.com/view/contact-non-prehensile}. | [] | [] | CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects | [
"Yoonyoung Cho",
"Junhyek Han",
"Yoontae Cho",
"Beomjoon Kim"
] | 2403.10760 | 18,901 | https://openreview.net/forum?id=KTtEICH4TO |
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[] | Spotlight Poster | [] | Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well? More specifically, do the graph structure and the node features carry enough usable information for the task? Our goals are(1) to develop a fast tool to measure how much information is in the graph structure and in the node features, and(2) to exploit the information to solve the task, if there is enough.We propose NetInfoF, a framework including NetInfoF_Probe and NetInfoF_Act, for the measurement and the exploitation of network usable information (NUI), respectively. Given a graph data, NetInfoF_Probe measures NUI without any model training, and NetInfoF_Act solves link prediction and node classification, while two modules share the same backbone.In summary, NetInfoF has following notable advantages:(a) General, handling both link prediction and node classification;(b) Principled, with theoretical guarantee and closed-form solution;(c) Effective, thanks to the proposed adjustment to node similarity;(d) Scalable, scaling linearly with the input size.In our carefully designed synthetic datasets, NetInfoF correctly identifies the ground truth of NUI and is the only method being robust to all graph scenarios. Applied on real-world datasets, NetInfoF wins in 11 out of 12 times on link prediction compared to general GNN baselines. | [] | [] | NetInfoF Framework: Measuring and Exploiting Network Usable Information | [
"Meng-Chieh Lee",
"Haiyang Yu",
"Jian Zhang",
"Vassilis N. Ioannidis",
"Xiang song",
"Soji Adeshina",
"Da Zheng",
"Christos Faloutsos"
] | 2402.07999 | 18,898 | https://openreview.net/forum?id=KY8ZNcljVU |
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[] | Poster | [] | The cost of hyperparameter tuning in deep learning has been rising with model sizes, prompting practitioners to find new tuning methods using a proxy of smaller networks. One such proposal uses $\mu$P parameterized networks, where the optimal hyperparameters for small width networks *transfer* to networks with arbitrarily large width. However, in this scheme, hyperparameters do not transfer across depths. As a remedy, we study residual networks with a residual branch scale of $1/\sqrt{\text{depth}}$ in combination with the $\mu$P parameterization. We provide experiments demonstrating that residual architectures including convolutional ResNets and vision transformers trained with this parameterization exhibit transfer of optimal hyperparameters across width and depth on CIFAR-10 and ImageNet. Furthermore, our empirical findings are supported and motivated by theory. Using recent developments in the dynamical mean field theory (DMFT) description of neural network learning dynamics, we show that this parameterization of ResNets admits a well-defined feature learning joint infinite-width and infinite-depth limit and show convergence of finite-size network dynamics towards this limit. | [] | [] | Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit | [
"Blake Bordelon",
"Lorenzo Noci",
"Mufan Bill Li",
"Boris Hanin",
"Cengiz Pehlevan"
] | 2309.16620 | 18,897 | https://openreview.net/forum?id=KZJehvRKGD |
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[] | Poster | [] | The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016). More specifically, the target variable $y$ (also known as the response variable), which is continuous, has different marginal distributions in the training source and testing domain, while the conditional distribution of features $\boldsymbol{x}$ given $y$ remains the same. While most literature focuses on classification tasks with finite target space, the regression problem has an *infinite dimensional* target space, which makes many of the existing methods inapplicable. In this work, we show that the continuous target shift problem can be addressed by estimating the importance weight function from an ill-posed integral equation. We propose a nonparametric regularized approach named *ReTaSA* to solve the ill-posed integral equation and provide theoretical justification for the estimated importance weight function. The effectiveness of the proposed method has been demonstrated with extensive numerical studies on synthetic and real-world datasets. | [] | [] | ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift | [
"Hwanwoo Kim",
"Xin Zhang",
"Jiwei Zhao",
"Qinglong Tian"
] | 2401.16410 | 18,894 | https://openreview.net/forum?id=KdVvOA00Or |
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[] | Poster | [] | Recent advances in language models (LMs) have led to significant improvements in quality on complex NLP tasks, but at the expense of increased inference costs. A simple strategy to achieve more favorable cost-quality tradeoffs is cascading: here, a small model is invoked for most “easy” instances, while a few “hard” instances are deferred to the large model. While the principles underpinning effective cascading are well-studied for classification tasks — with deferral based on predicted class uncertainty favored theoretically and practically — a similar understanding is lacking for generative LM tasks. In this work, we initiate a systematic study of deferral rules for LM cascades. We begin by examining the natural extension of predicted class uncertainty to generative LM tasks, namely, the predicted sequence uncertainty. We show that this measure suffers from the length bias problem, either over- or under-emphasizing outputs based on their lengths. This is because LMs produce a sequence of uncertainty values, one for each output token; and moreover, the number of output tokens is variable across different examples. To mitigate the length bias, we propose to exploit the richer token-level uncertainty information implicit in generative LMs. We argue that naive predicted sequence uncertainty corresponds to a simple aggregation of these uncertainties. By contrast, we show that incorporating token-level uncertainty through learned post-hoc deferral rules can significantly outperform such simple aggregation strategies, via experiments on a range of natural language benchmarks with FLAN-T5 models. We further show that incorporating embeddings from the smaller model and intermediate layers of the larger model can give an additional boost in the overall cost-quality tradeoff. | [] | [] | Language Model Cascades: Token-Level Uncertainty And Beyond | [
"Neha Gupta",
"Harikrishna Narasimhan",
"Wittawat Jitkrittum",
"Ankit Singh Rawat",
"Aditya Krishna Menon",
"Sanjiv Kumar"
] | 2404.10136 | 18,893 | https://openreview.net/forum?id=KgaBScZ4VI |
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[] | Poster | [] | Synthesizing novel views for dynamic scenes from a collection of RGB inputs poses significant challenges due to the inherent under-constrained nature of the problem. To mitigate this ill-posedness, practitioners in the field of neural radiance fields (NeRF) often resort to the adoption of intricate geometric regularization techniques, including scene flow, depth estimation, or learned perceptual similarity. While these geometric cues have demonstrated their effectiveness, their incorporation leads to evaluation of computationally expensive off-the-shelf models, introducing substantial computational overhead into the pipeline. Moreover, seamlessly integrating such modules into diverse dynamic NeRF models can be a non-trivial task, hindering their utilization in an architecture-agnostic manner. In this paper, we propose a theoretically grounded, lightweight regularizer by treating the dynamics of a time-varying scene as a low-frequency change of a probability distribution of the light intensity. We constrain the dynamics of this distribution using optimal transport (OT) and provide error bounds under reasonable assumptions. Our regularization is learning-free, architecture agnostic, and can be implemented with just a few lines of code. Finally, we demonstrate the practical efficacy of our regularizer across state-of-the-art architectures. | [] | [] | Improving the Convergence of Dynamic NeRFs via Optimal Transport | [
"Sameera Ramasinghe",
"Violetta Shevchenko",
"Gil Avraham",
"Hisham Husain",
"Anton van den Hengel"
] | 18,892 | https://openreview.net/forum?id=KiespDPaRH |
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[] | Spotlight Poster | [] | 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. | [] | [] | Cascading Reinforcement Learning | [
"Yihan Du",
"R. Srikant",
"Wei Chen"
] | 2401.08961 | 18,891 | https://openreview.net/forum?id=KjOAHlKMF5 |
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[] | Poster | [
"https://github.com/yifanlu0227/HEAL"
] | Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current works consider a homogeneous scenario where all agents use identity sensors and perception models. In reality, heterogeneous agent types may continually emerge and inevitably face a domain gap when collaborating with existing agents. In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost? To address this problem, we propose HEterogeneous ALliance (HEAL), a novel extensible collaborative perception framework. HEAL first establishes a unified feature space with initial agents via a novel multi-scale foreground-aware Pyramid Fusion network. When heterogeneous new agents emerge with previously unseen modalities or models, we align them to the established unified space with an innovative backward alignment. This step only involves individual training on the new agent type, thus presenting extremely low training costs and high extensibility. To enrich agents' data heterogeneity, we bring OPV2V-H, a new large-scale dataset with more diverse sensor types. Extensive experiments on OPV2V-H and DAIR-V2X datasets show that HEAL surpasses SOTA methods in performance while reducing the training parameters by 91.5\% when integrating 3 new agent types. We further implement a comprehensive codebase at: https://github.com/yifanlu0227/HEAL | [] | [] | An Extensible Framework for Open Heterogeneous Collaborative Perception | [
"Yifan Lu",
"Yue Hu",
"Yiqi Zhong",
"Dequan Wang",
"Yanfeng Wang",
"Siheng Chen"
] | 2401.13964 | 18,889 | https://openreview.net/forum?id=KkrDUGIASk |
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[] | Poster | [] | The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimization is one of these critical challenges. Amongst the diverse adjustments in hyperparameters, the adaptation of the learning rate emerges as a crucial component, holding the promise of significantly enhancing the efficacy of FL systems. In response to this critical need, this paper presents FedHyper, a novel hypergradient-based learning rate adaptation algorithm specifically designed for FL. FedHyper serves as a universal learning rate scheduler that can adapt both global and local rates as the training progresses. In addition, FedHyper not only showcases unparalleled robustness to a spectrum of initial learning rate configurations but also significantly alleviates the necessity for laborious empirical learning rate adjustments. We provide a comprehensive theoretical analysis of FedHyper’s convergence rate and conduct extensive experiments on vision and language benchmark datasets. The results demonstrate that FEDHYPER consistently converges 1.1-3× faster than FedAvg and the competing baselines while achieving superior final accuracy. Moreover, FEDHYPER catalyzes a remarkable surge in accuracy, augmenting it by up to 15% compared to FedAvg under suboptimal initial learning rate settings. | [] | [] | FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent | [
"Ziyao Wang",
"Jianyu Wang",
"Ang Li"
] | 2310.03156 | 18,888 | https://openreview.net/forum?id=Kl9CqKf7h6 |
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[] | Spotlight Poster | [] | Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence intrinsically contradicts the Markov nature of algorithmic reasoning tasks. Based on this motivation, we present our ForgetNet, which does not use historical embeddings and thus is consistent with the Markov nature of the tasks. To address challenges in training ForgetNet at early stages, we further introduce G-ForgetNet, which uses a gating mechanism to allow for the selective integration of historical embeddings. Such an enhanced capability could provide valuable guidance during the model's early training phase. Our extensive experiments, based on the CLRS-30 algorithmic reasoning benchmark, demonstrate that both ForgetNet and G-ForgetNet achieve better generalization capability than existing methods. Furthermore, we investigate the behavior of the gating mechanism, highlighting its degree of alignment with our intuitions and its effectiveness for robust performance. | [] | [] | On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods | [
"Montgomery Bohde",
"Meng Liu",
"Alexandra Saxton",
"Shuiwang Ji"
] | 2403.04929 | 18,887 | https://openreview.net/forum?id=Kn7tWhuetn |
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[] | Poster | [] | Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate high-fidelity synthetic data. In speech, text-to-speech synthesis and neural vocoder are good examples where generative models have shined. While generative models have been applied to different applications in speech, there exists no general-purpose generative model that models speech directly. In this work, we take a step toward this direction by showing a single pre-trained generative model can be adapted to different downstream tasks with strong performance. Specifically, we pre-trained a generative model, named SpeechFlow, on 60k hours of untranscribed speech with Flow Matching and masked conditions. Experiment results show the pre-trained generative model can be fine-tuned with task-specific data to match or surpass existing expert models on speech enhancement, separation, and synthesis. Our work suggested a foundational model for generation tasks in speech can be built with generative pre-training. | [] | [] | Generative Pre-training for Speech with Flow Matching | [
"Alexander H. Liu",
"Matthew Le",
"Apoorv Vyas",
"Bowen Shi",
"Andros Tjandra",
"Wei-Ning Hsu"
] | 2310.16338 | 18,885 | https://openreview.net/forum?id=KpoQSgxbKH |
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[] | Spotlight Poster | [] | A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE) dynamical system with different unknown ODE parameters, and demand accurate forecasts even under out-of-support initial conditions and out-of-support ODE parameters. In this work we propose to improve the OOD robustness of PIML via a meta-learning procedure for causal structure discovery. Using three different OOD tasks, we empirically observe that the proposed approach significantly outperforms existing state-of-the-art PIML and deep learning methods (with $2\times$ to $28\times$ lower OOD errors). | [] | [] | MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning | [
"S Chandra Mouli",
"Muhammad Alam",
"Bruno Ribeiro"
] | 18,883 | https://openreview.net/forum?id=KrWuDiW4Qm |
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[] | Poster | [] | Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply remembering the full history is generally intractable due to the exponential growth in the history space. Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable. While SOTA algorithms use Recurrent Neural Networks to compress the observation-action history aiming to learn a sufficient statistic, they lack guarantees of success and can lead to sub-optimal policies. To overcome this, we propose the Wasserstein Belief Updater, an RL algorithm that learns a latent model of the POMDP and an approximation of the belief update. Our approach comes with theoretical guarantees on the quality of our approximation ensuring that our outputted beliefs allow for learning the optimal value function. | [] | [] | The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models | [
"Raphaël Avalos",
"Florent Delgrange",
"Ann Nowe",
"Guillermo Perez",
"Diederik M Roijers"
] | 2303.03284 | 18,882 | https://openreview.net/forum?id=KrtGfTGaGe |
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[] | Spotlight Poster | [] | In this work, we aim to teach robots to manipulate various thin-shell materials. Prior works studying thin-shell object manipulation mostly rely on heuristic policies or learn policies from real-world video demonstrations, and only focus on limited material types and tasks (e.g., cloth unfolding). However, these approaches face significant challenges when extended to a wider variety of thin-shell materials and a diverse range of tasks.On the other hand, while virtual simulations are shown to be effective in diverse robot skill learning and evaluation, prior thin-shell simulation environments only support a subset of thin-shell materials, which also limits their supported range of tasks. To fill in this gap, we introduce ThinShellLab - a fully differentiable simulation platform tailored for robotic interactions with diverse thin-shell materials possessing varying material properties, enabling flexible thin-shell manipulation skill learning and evaluation. Building on top of our developed simulation engine, we design a diverse set of manipulation tasks centered around different thin-shell objects. Our experiments suggest that manipulating thin-shell objects presents several unique challenges: 1) thin-shell manipulation relies heavily on frictional forces due to the objects' co-dimensional nature, 2) the materials being manipulated are highly sensitive to minimal variations in interaction actions, and 3) the constant and frequent alteration in contact pairs makes trajectory optimization methods susceptible to local optima, and neither standard reinforcement learning algorithms nor trajectory optimization methods (either gradient-based or gradient-free) are able to solve the tasks alone. To overcome these challenges, we present an optimization scheme that couples sampling-based trajectory optimization and gradient-based optimization, boosting both learning efficiency and converged performance across various proposed tasks. In addition, the differentiable nature of our platform facilitates a smooth sim-to-real transition. By tuning simulation parameters with a minimal set of real-world data, we demonstrate successful deployment of the learned skills to real-robot settings. ThinShellLab will be publicly available. Video demonstration and more information can be found on the project website https://thinshelllab.github.io. | [] | [] | Thin-Shell Object Manipulations With Differentiable Physics Simulations | [
"Yian Wang",
"Juntian Zheng",
"Zhehuan Chen",
"Zhou Xian",
"Gu Zhang",
"Chao Liu",
"Chuang Gan"
] | 2404.00451 | 18,881 | https://openreview.net/forum?id=KsUh8MMFKQ |
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[] | Spotlight Poster | [
"https://github.com/OpenBMB/VisCPM.git"
] | Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English, leaving other languages largely behind. Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i.e., lack of large-scale, high-quality image-text data). In this work, we propose \trainname, an effective training paradigm for training large multimodal models in low-resource languages. \trainname demonstrates that \textbf{M}ultilingual language models can \textbf{P}ivot zero-shot \textbf{M}ultimodal learning across languages. Specifically, based on a strong multilingual large language model, multimodal models pretrained on English-only image-text data can well generalize to other languages in a zero-shot manner, even surpassing models trained on image-text data in native languages. Taking Chinese as a practice of \trainname, we build large multimodal models \modelname in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese. To facilitate future research, we open-source codes and model weights at https://anonymous.4open.science/r/VisCPM-8E13. | [] | [] | Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages | [
"Jinyi Hu",
"Yuan Yao",
"Chongyi Wang",
"SHAN WANG",
"Yinxu Pan",
"Qianyu Chen",
"Tianyu Yu",
"Hanghao Wu",
"Yue Zhao",
"Haoye Zhang",
"Xu Han",
"Yankai Lin",
"Jiao Xue",
"dahai li",
"Zhiyuan Liu",
"Maosong Sun"
] | 2308.12038 | 18,879 | https://openreview.net/forum?id=Kuh5qgCGCp |
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[] | Poster | [] | Computing the optimal transport distance between statistical distributions is a fundamental task in machine learning. One remarkable recent advancement is entropic regularization and the Sinkhorn algorithm, which utilizes only matrix scaling and guarantees an approximated solution with near-linear runtime. Despite the success of the Sinkhorn algorithm, its runtime may still be slow due to the potentially large number of iterations needed for convergence. To achieve possibly super-exponential convergence, we introduce Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine. Adopting the variational viewpoint that the Sinkhorn algorithm maximizes a concave Lyapunov potential, we offer the insight that the Hessian matrix of the potential function is approximately sparse. Sparsification of the Hessian results in a fast $O(n^2)$ per-iteration complexity, the same as the Sinkhorn algorithm. In terms of total iteration count, we observe that the SNS algorithm converges orders of magnitude faster across a wide range of practical cases, including optimal transportation between empirical distributions and calculating the Wasserstein $W_1, W_2$ distance of discretized continuous densities. The empirical performance is corroborated by a rigorous bound on the approximate sparsity of the Hessian matrix. | [] | [] | Accelerating Sinkhorn algorithm with sparse Newton iterations | [
"Xun Tang",
"Michael Shavlovsky",
"Holakou Rahmanian",
"Elisa Tardini",
"Kiran Koshy Thekumparampil",
"Tesi Xiao",
"Lexing Ying"
] | 2401.12253 | 18,878 | https://openreview.net/forum?id=Kuj5gVp5GQ |
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[] | Spotlight Poster | [] | An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others). In this work, we critically analyze this approach of imitating language models.We first finetune a series of LMs that imitate ChatGPT using varying base model sizes (1.5B--13B), data sources, and imitation data amounts (0.3M--150M tokens). We then evaluate the models using crowd raters and canonical NLP benchmarks. Initially, we were surprised by the output quality of our imitation models---they appear far better at following instructions, and crowd workers rate their outputs as competitive with ChatGPT. However, when conducting more targeted automatic evaluations, we find that imitation models close little to none of the gap from the base LM to ChatGPT on tasks that are not heavily supported in the imitation data. We show that these performance discrepancies may slip past human raters because imitation models are adept at mimicking ChatGPT’s style but not its factuality. Overall, we conclude that model imitation is a false promise: there exists a substantial capabilities gap between open and closed LMs that, with current methods, can only be bridged using an unwieldy amount of imitation data or by using more capable base LMs.In turn, we argue that the highest leverage action for improving open-source models is to tackle the difficult challenge of developing better base LMs, rather than taking the shortcut of imitating proprietary systems. | [] | [] | The False Promise of Imitating Proprietary Language Models | [
"Arnav Gudibande",
"Eric Wallace",
"Charlie Victor Snell",
"Xinyang Geng",
"Hao Liu",
"Pieter Abbeel",
"Sergey Levine",
"Dawn Song"
] | 18,877 | https://openreview.net/forum?id=Kz3yckpCN5 |
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[] | Poster | [] | An increasing number of vision-language tasks can be handled with little to no training (i.e., in a zero and few-shot manner) by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs). While this has huge upsides (e.g., not requiring training data or custom architectures), how an input is presented to a LVLM can have a major impact on zero-shot model performance. In particular, inputs phrased in an underspecified way can result in incorrect answers due to factors like missing visual information, complex implicit reasoning, or linguistic ambiguity. Therefore, adding visually-grounded information should improve model performance by reducing underspecification, e.g., by localizing objects and disambiguating references. To this end, we present **Rep**hrase, **A**ugment and **Re**ason (RepARe), a gradient-free framework, which extracts salient details about the image using the underlying LVLM as a captioner and reasoner, in order to propose modifications to the original question. We then use the LVLM’s confidence over a generated answer as an unsupervised scoring function to select the rephrased question most likely to improve zero-shot performance. Focusing on two visual question answering tasks, we show that RepARe can result in an 3.85 percentage point (absolute) increase in zero-shot performance on VQAv2 and a 6.41 point increase on A-OKVQA. Additionally, we find that using gold answers for oracle selection of question candidates achieves an impressive gain in VQA accuracy by up to 14.41 percentage points. Through extensive analysis, we demonstrate that outputs from RepARe increase syntactic complexity and better utilize the frozen language model in LVLMs. | [] | [] | Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models | [
"Archiki Prasad",
"Elias Stengel-Eskin",
"Mohit Bansal"
] | 2310.05861 | 18,874 | https://openreview.net/forum?id=L4nOxziGf9 |
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[] | Poster | [] | AI agents are commonly trained with large datasets of demonstrations of human behavior.However, not all behaviors are equally safe or desirable.Desired characteristics for an AI agent can be expressed by assigning desirability scores, which we assume are assigned to collective trajectories, but not to individual behaviors.For example, in a dataset of vehicle interactions, these scores might relate to the number of incidents that occurred. We first assess the effect of each individual agent's behavior on the collective desirability score, e.g., assessing how likely an agent is to cause incidents.This allows us to afterward only imitate agents with desired behavior, e.g., only imitating agents that are unlikely to cause incidents. To enable this, we propose the concept of an agent's \textit{Exchange Value}, which quantifies an individual agent's contribution to the collective desirability score. This is expressed as the expected change in desirability score when substituting the agent for a randomly selected agent.We propose additional methods for estimating Exchange Values from real-world datasets, enabling us to learn aligned imitation policies that outperform relevant baselines. | [] | [] | Select to Perfect: Imitating desired behavior from large multi-agent data | [
"Tim Franzmeyer",
"Edith Elkind",
"Philip Torr",
"Jakob Nicolaus Foerster",
"Joao F. Henriques"
] | 18,872 | https://openreview.net/forum?id=L6crLU7MIE |
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[] | Spotlight Poster | [] | In this study, we investigate the DIstribution Correction Estimation (DICE) methods, an important line of work in offline reinforcement learning (RL) and imitation learning (IL). DICE-based methods impose state-action-level behavior constraint, which is an ideal choice for offline learning. However, they typically perform much worse than current state-of-the-art (SOTA) methods that solely use action-level behavior constraint. After revisiting DICE-based methods, we find there exist two gradient terms when learning the value function using true-gradient update: forward gradient (taken on the current state) and backward gradient (taken on the next state). Using forward gradient bears a large similarity to many offline RL methods, and thus can be regarded as applying action-level constraint. However, directly adding the backward gradient may degenerate or cancel out its effect if these two gradients have conflicting directions. To resolve this issue, we propose a simple yet effective modification that projects the backward gradient onto the normal plane of the forward gradient, resulting in an orthogonal-gradient update, a new learning rule for DICE-based methods. We conduct thorough theoretical analyses and find that the projected backward gradient brings state-level behavior regularization, which reveals the mystery of DICE-based methods: the value learning objective does try to impose state-action-level constraint, but needs to be used in a corrected way. Through toy examples and extensive experiments on complex offline RL and IL tasks, we demonstrate that DICE-based methods using orthogonal-gradient updates achieve SOTA performance and great robustness. | [] | [] | ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update | [
"Liyuan Mao",
"Haoran Xu",
"Weinan Zhang",
"Xianyuan Zhan"
] | 2402.00348 | 18,871 | https://openreview.net/forum?id=L8UNn7Llt4 |
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[] | Poster | [] | We introduce a generative model with an intrinsically interpretable layer---a concept bottleneck layer---that constrains the model to encode human-understandable concepts. The concept bottleneck layer partitions the generative model into three parts: the pre-concept bottleneck portion, the CB layer, and the post-concept bottleneck portion. To train CB generative models, we complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss. The CB layer and these loss terms are model agnostic, which we demonstrate by applying the CB layer to three different families of generative models: generative adversarial networks, variational autoencoders, and diffusion models. On multiple datasets across different types of generative models, steering a generative model, with the CB layer, outperforms all baselines---in some cases, it is \textit{10 times} more effective. In addition, we show how the CB layer can be used to interpret the output of the generative model and debug the model during or post training. | [] | [] | Concept Bottleneck Generative Models | [
"Aya Abdelsalam Ismail",
"Julius Adebayo",
"Hector Corrada Bravo",
"Stephen Ra",
"Kyunghyun Cho"
] | 18,870 | https://openreview.net/forum?id=L9U5MJJleF |
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[] | Poster | [] | Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training. Prior work in model merging permutes one model to the space of the other then averages them together. While this works for models trained on the same task, we find that this fails to account for the differences in models trained on disjoint tasks. Thus, we introduce "ZipIt!", a general method for merging two arbitrary models of the same architecture that incorporates two simple strategies. First, in order to account for features that aren’t shared between models, we expand the model merging problem to allow for merging features within each model by defining a general “zip” operation. Second, we add support for partially zipping the models up until a specified layer, naturally creating a multi-head model. We find that these two changes combined account for a staggering 20-60% improvement over prior work, making it feasible to merge models trained on disjoint tasks without retraining. | [] | [] | ZipIt! Merging Models from Different Tasks without Training | [
"George Stoica",
"Daniel Bolya",
"Jakob Brandt Bjorner",
"Pratik Ramesh",
"Taylor Hearn",
"Judy Hoffman"
] | 2305.03053 | 18,869 | https://openreview.net/forum?id=LEYUkvdUhq |
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[] | Poster | [] | Alignment with human preference is a desired property of large language models (LLMs). Currently, the main alignment approach is based on reinforcement learning from human feedback (RLHF). Despite the effectiveness of RLHF, it is intricate to implement and train, thus recent studies explore how to develop alternative alignment approaches based on supervised fine-tuning (SFT). A major limitation of SFT is that it essentially does imitation learning, which can't fully understand what are the expected behaviors. To address this issue, we propose an improved alignment approach named $\textbf{FIGA}$. Different from prior methods, we incorporate fine-grained (i.e., token or phrase level) quality signals that are derived by contrasting good and bad responses. Our approach has made two major contributions. Firstly, we curate a refined alignment dataset that pairs initial responses and the corresponding revised ones. Secondly, we devise a new loss function can leverage fine-grained quailty signals to instruct the learning of LLMs for alignment. Extensive experiments have demonstrated the effectiveness of our approaches by comparing a number of competitive baselines. | [] | [] | Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment | [
"Geyang Guo",
"Ranchi Zhao",
"Tianyi Tang",
"Xin Zhao",
"Ji-Rong Wen"
] | 2311.04072 | 18,867 | https://openreview.net/forum?id=LNLjU5C5dK |
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[] | Poster | [] | We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding error and disturbances. While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond expert data. The key insight is to leverage local continuity in the environment dynamics. Our method first constructs a dynamics model from the expert demonstration, enforcing local Lipschitz continuity while skipping the discontinuous regions. In the locally continuous regions, this model allows us to generate corrective labels within the neighborhood of the demonstrations but beyond the actual set of states and actions in the dataset. Training on this augmented data enhances the agent's ability to recover from perturbations and deal with compounding error. We demonstrate the effectiveness of our generated labels through experiments in a variety of robotics domains that have distinct forms of continuity and discontinuity, including classic control, drone flying, high-dimensional navigation, locomotion, and tabletop manipulation. | [] | [] | CCIL: Continuity-Based Data Augmentation for Corrective Imitation Learning | [
"Liyiming Ke",
"Yunchu Zhang",
"Abhay Deshpande",
"Siddhartha Srinivasa",
"Abhishek Gupta"
] | 2310.12972 | 18,866 | https://openreview.net/forum?id=LQ6LQ8f4y8 |
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[] | Poster | [] | We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., $\ell^1$ or $\ell^2$, such functionals provide more flexibility and allow using auxiliary information, such as class labels, to construct the required transport map. Existing methods for general costs are discrete and have limitations in practice, i.e. they do not provide an out-of-sample estimation. We address the challenge of designing a continuous OT approach for general costs that generalizes to new data points in high-dimensional spaces, such as images. Additionally, we provide the theoretical error analysis for our recovered transport plans. As an application, we construct a cost functional to map data distributions while preserving the class-wise structure. | [] | [] | Neural Optimal Transport with General Cost Functionals | [
"Arip Asadulaev",
"Alexander Korotin",
"Vage Egiazarian",
"Petr Mokrov",
"Evgeny Burnaev"
] | 2205.15403 | 18,148 | https://openreview.net/forum?id=gIiz7tBtYZ |
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[] | Poster | [] | We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis shows that the need for features to evolve across layers results in temporally misaligned features in skip connections, which limits the model’s performance. To address this limitation, we propose SineNet, consisting of multiple sequentially connected U-shaped network blocks, referred to as waves. In SineNet, high-resolution features are evolved progressively through multiple stages, thereby reducing the amount of misalignment within each stage. We furthermore analyze the role of skip connections in enabling both parallel and sequential processing of multi-scale information. Our method is rigorously tested on multiple PDE datasets, including the Navier-Stokes equations and shallow water equations, showcasing the advantages of our proposed approach over conventional U-Nets with a comparable parameter budget. We further demonstrate that increasing the number of waves in SineNet while maintaining the same number of parameters leads to a monotonically improved performance. The results highlight the effectiveness of SineNet and the potential of our approach in advancing the state-of-the-art in neural PDE solver design. | [] | [] | SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations | [
"Xuan Zhang",
"Jacob Helwig",
"Yuchao Lin",
"Yaochen Xie",
"Cong Fu",
"Stephan Wojtowytsch",
"Shuiwang Ji"
] | 2403.19507 | 18,865 | https://openreview.net/forum?id=LSYhE2hLWG |
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[] | Poster | [] | Transformer architecture search (TAS) has achieved remarkable progress in automating the neural architecture design process of vision transformers. Recent TAS advancements have discovered outstanding transformer architectures while saving tremendous labor from human experts. However, it is still cumbersome to deploy these methods in real-world applications due to the expensive costs of data labeling under the supervised learning paradigm. To this end, this paper proposes a masked image modelling (MIM) based self-supervised neural architecture search method specifically designed for vision transformers, termed as MaskTAS, which completely avoids the expensive costs of data labeling inherited from supervised learning. Based on the one-shot NAS framework, MaskTAS requires to train various weight-sharing subnets, which can easily diverged without strong supervision in MIM-based self-supervised learning.For this issue, we design the search space of MaskTAS as a siamesed teacher-student architecture to distill knowledge from pre-trained networks, allowing for efficient training of the transformer supernet.To achieve self-supervised transformer architecture search, we further design a novel unsupervised evaluation metric for the evolutionary search algorithm, where each candidate of the student branch is rated by measuring its consistency with the larger teacher network.Extensive experiments demonstrate that the searched architectures can achieve state-of-the-art accuracy on benchmark dataset even without using manual labels. Moreover, the proposed MaskTAS can generalize well to various data domains and tasks by searching specialized transformer architectures in self-supervised manner. | [] | [] | Masked Distillation Advances Self-Supervised Transformer Architecture Search | [
"Caixia Yan",
"Xiaojun Chang",
"Zhihui Li",
"Lina Yao",
"Minnan Luo",
"Qinghua Zheng"
] | 18,864 | https://openreview.net/forum?id=LUpC8KTvdV |
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[] | Poster | [
"https://github.com/NJU-RL/ACORM](https:"
] | Real-world multi-agent tasks usually involve dynamic team composition with the emergence of roles, which should also be a key to efficient cooperation in multi-agent reinforcement learning (MARL). Drawing inspiration from the correlation between roles and agent's behavior patterns, we propose a novel framework of Attention-guided COntrastive Role representation learning for MARL (ACORM) to promote behavior heterogeneity, knowledge transfer, and skillful coordination across agents. First, we introduce mutual information maximization to formalize role representation learning, derive a contrastive learning objective, and concisely approximate the distribution of negative pairs. Second, we leverage an attention mechanism to prompt the global state to attend to learned role representations in value decomposition, implicitly guiding agent coordination in a skillful role space to yield more expressive credit assignment. Experiments and visualizations on challenging StarCraft II micromanagement tasks demonstrate the state-of-the-art performance of our method and its advantages over existing approaches. | [] | [] | Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning | [
"Zican Hu",
"Zongzhang Zhang",
"Huaxiong Li",
"Chunlin Chen",
"Hongyu Ding",
"Zhi Wang"
] | 2312.04819 | 18,863 | https://openreview.net/forum?id=LWmuPfEYhH |
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[] | Poster | [] | We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges. To overcome these obstacles, we propose a versatile black-box approach applicable to a broad spectrum of problems, such as general-sum games, potential games, and Markov games, when equipped with appropriate learning and testing oracles for stationary environments. Our algorithms can achieve $\widetilde{O}\left(\Delta^{1/4}T^{3/4}\right)$ regret when the degree of nonstationarity, as measured by total variation $\Delta$, is known, and $\widetilde{O}\left(\Delta^{1/5}T^{4/5}\right)$ regret when $\Delta$ is unknown, where $T$ is the number of rounds. Meanwhile, our algorithm inherits the favorable dependence on number of agents from the oracles. As a side contribution that may be independent of interest, we show how to test for various types of equilibria by a black-box reduction to single-agent learning, which includes Nash equilibria, correlated equilibria, and coarse correlated equilibria. | [] | [] | A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning | [
"Haozhe Jiang",
"Qiwen Cui",
"Zhihan Xiong",
"Maryam Fazel",
"Simon Shaolei Du"
] | 2306.07465 | 18,862 | https://openreview.net/forum?id=LWuYsSD94h |
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[] | Poster | [] | Convolution-based language models are asymptotically more efficient than Transformers and recent work shows they are competitive in quality. To better understand the relative language modeling quality of these architectures, we pre-train a suite of 14 language models across attention and convolution-based architectures, finding that the SoTA gated convolution architectures still underperform Transformers by up to 2.1 perplexity points on the Pile. Our analysis shows that a single language modeling capability, termed associative recall (AR) — output the next token using the prior context, e.g. Hakuna Matata means no worries Hakuna Matata it means no → ?? — accounts for 76% of the perplexity gap on average. We show the issue arises because the convolution-based models process sequences using fixed filters that do not depend on the input data, making it difficult to handle a variable number of input-specific recall distances (e.g. 4 tokens between instances of Hakuna vs. 5 between worries above). Theoretically, our core contributions are precise bounds for solving AR, applying to the entire class of gated convolution models, that show dimensionality scaling in sequence length. Meanwhile, attention enables tokens separated by any distance to interact and solves AR with model dimension independent of sequence length. We present (1) a concise synthetic AR task, on which we validate the theoretically predicted scaling holds, and (2) a series of architectural modifications, theoretically and empirically showing that they enable solving AR with improved scaling. Our analysis motivates a set of strong baseline models that outperform Transformers at 150M and 355M parameters. We release all checkpoints and code for future analysis. | [] | [] | Zoology: Measuring and Improving Recall in Efficient Language Models | [
"Simran Arora",
"Sabri Eyuboglu",
"Aman Timalsina",
"Isys Johnson",
"Michael Poli",
"James Zou",
"Atri Rudra",
"Christopher Re"
] | 18,860 | https://openreview.net/forum?id=LY3ukUANko |
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[] | Spotlight Poster | [] | Humanoid control is an important research challenge offering avenues for integration into human-centric infrastructures and enabling physics-driven humanoid animations.The daunting challenges in this field stem from the difficulty of optimizing in high-dimensional action spaces and the instability introduced by the bipedal morphology of humanoids. However, the extensive collection of human motion-captured data and the derived datasets of humanoid trajectories, such as MoCapAct, paves the way to tackle these challenges. In this context, we present Humanoid Generalist Autoencoding Planner (H-GAP), a state-action trajectory generative model trained on humanoid trajectories derived from human motion-captured data, capable of adeptly handling downstream control tasks with Model Predictive Control (MPC).For 56 degrees of freedom humanoid, we empirically demonstrate that H-GAP learns to represent and generate a wide range of motor behaviors. Further, without any learning from online interactions, it can also flexibly transfer these behaviours to solve novel downstream control tasks via planning. Notably, H-GAP excels established MPC baselines with access to the ground truth model, and is superior or comparable to offline RL methods trained for individual tasks.Finally, we do a series of empirical studies on the scaling properties of H-GAP, showing the potential for performance gains via additional data but not computing. | [] | [] | H-GAP: Humanoid Control with a Generalist Planner | [
"zhengyao jiang",
"Yingchen Xu",
"Nolan Wagener",
"Yicheng Luo",
"Michael Janner",
"Edward Grefenstette",
"Tim Rocktäschel",
"Yuandong Tian"
] | 18,859 | https://openreview.net/forum?id=LYG6tBlEX0 |
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[] | Poster | [
"https://github.com/facebookresearch/luckmatters"
] | We propose Joint MLP/Attention (JoMA) dynamics, a novel mathematical framework to understand the training procedure of multilayer Transformer architectures. This is achieved by integrating out the self-attention layer in Transformers, producing a modified dynamics of MLP layers only. JoMA removes unrealistic assumptions in previous analysis (e.g., lack of residual connection), and predicts that the attention first becomes sparse (to learn salient tokens), then dense (to learn less salient tokens) in the presence of nonlinear activations, while in the linear case, it is consistent with existing works. We leverage JoMA to qualitatively explains how tokens are combined to form hierarchies in multilayer Transformers, when the input tokens are generated by a latent hierarchical generative model. Experiments on models trained from real-world dataset (Wikitext2/Wikitext103) and various pre- trained models (OPT, Pythia) verify our theoretical findings. | [] | [] | JoMA: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention | [
"Yuandong Tian",
"Yiping Wang",
"Zhenyu Zhang",
"Beidi Chen",
"Simon Shaolei Du"
] | 2310.00535 | 18,857 | https://openreview.net/forum?id=LbJqRGNYCf |
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[] | Poster | [] | We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI). Our approach is based on the observation that when the sampling of variables in a PGM is seen as a sequence of actions taken by an agent, sparsity of the PGM enables local credit assignment in the agent's policy learning objective. This yields a local constraint that can be turned into a local loss in the style of generative flow networks (GFlowNets) that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably. The $\Delta$-AI objective matches the conditional distribution of a variable given its Markov blanket in a tractable learned sampler, which has the structure of a Bayesian network, with the same conditional distribution under the target PGM. As such, the trained sampler recovers marginals and conditional distributions of interest and enables inference of partial subsets of variables. We illustrate $\Delta$-AI's effectiveness for sampling from synthetic PGMs and training latent variable models with sparse factor structure. | [] | [] | Delta-AI: Local objectives for amortized inference in sparse graphical models | [
"Jean-Pierre René Falet",
"Hae Beom Lee",
"Nikolay Malkin",
"Chen Sun",
"Dragos Secrieru",
"Dinghuai Zhang",
"Guillaume Lajoie",
"Yoshua Bengio"
] | 18,855 | https://openreview.net/forum?id=LemSSn8htt |
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[] | Poster | [] | Fine-tuning text-to-image models using reward functions trained on human feedback data has emerged as a powerful approach for aligning model behavior with human intent. However, excessive optimization with such reward models, which are only proxy objectives, can degrade the performance of the fine-tuned models, a phenomenon commonly referred to as reward overoptimization. We introduce the Text-Image Alignment Assessment (TIA2) benchmark, a diverse collection of text prompts, images, and human annotations, for studying the issue in depth. We evaluate several state-of-the-art reward models for text-to-image generation on our benchmark and find that they are often not well-aligned with human assessment. We empirically demonstrate that overoptimization can occur when a poorly aligned reward model is used as a fine-tuning objective. To address this, we introduce a simple method, TextNorm, for inducing confidence calibration in reward models by normalizing the scores across prompts that are semantically different from the original prompt. We demonstrate that using the confidence-calibrated scores in fine-tuning effectively reduces the risk of overoptimization. | [] | [] | Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models | [
"Kyuyoung Kim",
"Jongheon Jeong",
"Minyong An",
"Mohammad Ghavamzadeh",
"Krishnamurthy Dj Dvijotham",
"Jinwoo Shin",
"Kimin Lee"
] | 2404.01863 | 18,854 | https://openreview.net/forum?id=Let8OMe20n |
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[] | Poster | [] | Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models. It is most prominently used in federated learning. If models are averaged at the end of training, this can only lead to a good performing model if the loss surface of interest is very particular, i.e., the loss in the exact middle between the two models needs to be sufficiently low. This is impossible to guarantee for the non-convex losses of state-of-the-art networks. For averaging models trained on vastly different datasets, it was proposed to average only the parameters of particular layers or combinations of layers, resulting in better performing models. To get a better understanding of the effect of layer-wise averaging, we analyse the performance of the models that result from averaging single layers, or groups of layers. Based on our empirical and theoretical investigation, we introduce a novel notion of the layer-wise linear connectivity, and show that deep networks do not have layer-wise barriers between them. We analyze additionally the layer-wise personalization averaging and conjecture that in particular problem setup all the partial aggregations result in the approximately same performance. | [] | [] | Layer-wise linear mode connectivity | [
"Linara Adilova",
"Maksym Andriushchenko",
"Michael Kamp",
"Asja Fischer",
"Martin Jaggi"
] | 2307.06966 | 18,853 | https://openreview.net/forum?id=LfmZh91tDI |
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[] | Poster | [] | Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities. Past attempts have been restricted to the case of fully-connected, convolutional, and residual networks. In this paper, we present a systematic approach for fusing two or more transformer-based networks exploiting Optimal Transport to (soft-)align the various architectural components. We flesh out an abstraction for layer alignment, that can generalize to arbitrary architectures -- in principle -- and we apply this to the key ingredients of Transformers such as multi-head self-attention, layer-normalization, and residual connections, and we discuss how to handle them via various ablation studies. Furthermore, our method allows the fusion of models of different sizes (heterogeneous fusion), providing a new and efficient way for compression of Transformers. The proposed approach is evaluated on both image classification tasks via Vision Transformer and natural language modeling tasks using BERT. Our approach consistently outperforms vanilla fusion, and, after a surprisingly short finetuning, also outperforms the individual converged parent models. In our analysis, we uncover intriguing insights about the significant role of soft alignment in the case of Transformers. Our results showcase the potential of fusing multiple Transformers, thus compounding their expertise, in the budding paradigm of model fusion and recombination. | [] | [] | Transformer Fusion with Optimal Transport | [
"Moritz Imfeld",
"Jacopo Graldi",
"Marco Giordano",
"Thomas Hofmann",
"Sotiris Anagnostidis",
"Sidak Pal Singh"
] | 2310.05719 | 18,852 | https://openreview.net/forum?id=LjeqMvQpen |
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[] | Poster | [] | Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used in the image domain, where the pretext task is to restore features at the masked pixels, such as colors, the existing 3D MAE works reconstruct the missing geometry only, i.e, the location of the masked points. In contrast to previous studies, we advocate that point location recovery is inessential and restoring intrinsic point features is much superior. To this end, we propose to ignore point position reconstruction and recover high-order features at masked points including surface normals and surface variations, through a novel attention-based decoder which is independent of the encoder design. We validate the effectiveness of our pretext task and decoder design using different encoder structures for 3D training and demonstrate the advantages of our pretrained networks on various point cloud analysis tasks. | [] | [] | 3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining | [
"Siming Yan",
"Yuqi Yang",
"Yu-Xiao Guo",
"Hao Pan",
"Peng-Shuai Wang",
"Xin Tong",
"Yang Liu",
"Qixing Huang"
] | 2304.06911 | 18,849 | https://openreview.net/forum?id=LokR2TTFMs |
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[] | Spotlight Poster | [] | Bilevel optimization is an important formulation for many machine learning problems, such as meta-learning and hyperparameter optimization. Current bilevel optimization algorithms assume that the gradient of the upper-level function is Lipschitz (i.e., the upper-level function has a bounded smoothness parameter). However, recent studies reveal that certain neural networks such as recurrent neural networks (RNNs) and long-short-term memory networks (LSTMs) exhibit potential unbounded smoothness, rendering conventional bilevel optimization algorithms unsuitable for these neural networks. In this paper, we design a new bilevel optimization algorithm, namely BO-REP, to address this challenge. This algorithm updates the upper-level variable using normalized momentum and incorporates two novel techniques for updating the lower-level variable: \textit{initialization refinement} and \textit{periodic updates}. Specifically, once the upper-level variable is initialized, a subroutine is invoked to obtain a refined estimate of the corresponding optimal lower-level variable, and the lower-level variable is updated only after every specific period instead of each iteration. When the upper-level problem is nonconvex and unbounded smooth, and the lower-level problem is strongly convex, we prove that our algorithm requires $\widetilde{\mathcal{O}}(1/\epsilon^4)$ \footnote{Here $\widetilde{\mathcal{O}}(\cdot)$ compresses logarithmic factors of $1/\epsilon$ and $1/\delta$, where $\delta\in(0,1)$ denotes the failure probability.} iterations to find an $\epsilon$-stationary point in the stochastic setting, where each iteration involves calling a stochastic gradient or Hessian-vector product oracle. Notably, this result matches the state-of-the-art complexity results under the bounded smoothness setting and without mean-squared smoothness of the stochastic gradient, up to logarithmic factors. Our proof relies on novel technical lemmas for the periodically updated lower-level variable, which are of independent interest. Our experiments on hyper-representation learning, hyperparameter optimization, and data hyper-cleaning for text classification tasks demonstrate the effectiveness of our proposed algorithm. The code is available at [https://github.com/MingruiLiu-ML-Lab/Bilevel-Optimization-under-Unbounded-Smoothness](https://github.com/MingruiLiu-ML-Lab/Bilevel-Optimization-under-Unbounded-Smoothness). | [] | [] | Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis | [
"Jie Hao",
"Xiaochuan Gong",
"Mingrui Liu"
] | 2401.09587 | 18,848 | https://openreview.net/forum?id=LqRGsGWOTX |
|
[] | Poster | [
"https://github.com/OpenDriveLab/LaneSegNet"
] | A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, i.e., map element detection (+4.8 mAP), centerline perception (+6.9 DET_l), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code would be released to the public. | [] | [] | LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving | [
"Tianyu Li",
"Peijin Jia",
"Bangjun Wang",
"Li Chen",
"KUN JIANG",
"Junchi Yan",
"Hongyang Li"
] | 2312.16108 | 18,846 | https://openreview.net/forum?id=LsURkIPYR5 |
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[] | Poster | [] | This paper introduces Instruction-oriented Object Detection (IOD), a new task that enhances human-computer interaction by enabling object detectors to understand user instructions and locate relevant objects. Unlike traditional open-vocabulary object detection tasks that rely on users providing a list of required category names, IOD requires models to comprehend natural-language instructions, contextual reasoning, and output the name and location of the desired categories. This poses fresh challenges for modern object detection systems. To develop an IOD system, we create a dataset called IOD-Bench, which consists of instruction-guided detections, along with specialized evaluation metrics. We leverage large-scale language models (LLMs) to generate a diverse set of instructions (8k+) based on existing public object detection datasets, covering a wide range of real-world scenarios. As an initial approach to the IOD task, we propose a model called Ins-DetCLIP. It harnesses the extensive knowledge within LLMs to empower the detector with instruction-following capabilities. Specifically, our Ins-DetCLIP employs a visual encoder (i.e., DetCLIP, an open-vocabulary detector) to extract object-level features. These features are then aligned with the input instructions using a cross-modal fusion module integrated into a pre-trained LLM. Experimental results conducted on IOD-Bench demonstrate that our model consistently outperforms baseline methods that directly combine LLMs with detection models. This research aims to pave the way for a more adaptable and versatile interaction paradigm in modern object detection systems, making a significant contribution to the field. | [] | [] | Ins-DetCLIP: Aligning Detection Model to Follow Human-Language Instruction | [
"Renjie Pi",
"Lewei Yao",
"Jianhua Han",
"Xiaodan Liang",
"Wei Zhang",
"Hang Xu"
] | 18,842 | https://openreview.net/forum?id=M0MF4t3hE9 |
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[] | Poster | [] | Posterior sampling allows the exploitation of prior knowledge of the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typically specified as a class of parametric distributions, a task that can be cumbersome in practice, often resulting in the choice of uninformative priors. In this work, we propose a novel posterior sampling approach in which the prior is given as a (partial) causal graph over the environment's variables. The latter is often more natural to design, such as listing known causal dependencies between biometric features in a medical treatment study. Specifically, we propose a hierarchical Bayesian procedure, called C-PSRL, simultaneously learning the full causal graph at the higher level and the parameters of the resulting factored dynamics at the lower level. For this procedure, we provide an analysis of its Bayesian regret, which explicitly connects the regret rate with the degree of prior knowledge. Our numerical evaluation conducted in illustrative domains confirms that C-PSRL strongly improves the efficiency of posterior sampling with an uninformative prior while performing close to posterior sampling with the full causal graph. | [] | [] | Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning | [
"Mirco Mutti",
"Riccardo De Santi",
"Marcello Restelli",
"Alexander Marx",
"Giorgia Ramponi"
] | 2310.07518 | 18,841 | https://openreview.net/forum?id=M0xK8nPGvt |
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[] | Poster | [] | Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs. Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback. However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases. In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue problems with a tunable language model plug-in as a plug-and-play dialogue policy planner, named PPDPP. Specifically, we develop a novel training framework to facilitate supervised fine-tuning over available human-annotated data as well as reinforcement learning from goal-oriented AI feedback with dynamic interaction data collected by the LLM-based self-play simulation. In this manner, the LLM-powered dialogue agent can not only be generalized to different cases after the training, but also be applicable to different applications by just substituting the learned plug-in. In addition, we propose to evaluate the policy planning capability of dialogue systems under the interactive setting. Experimental results demonstrate that PPDPP consistently and substantially outperforms existing approaches on three different proactive dialogue applications, including negotiation, emotional support, and tutoring dialogues. | [] | [] | Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents | [
"Yang Deng",
"Wenxuan Zhang",
"Wai Lam",
"See-Kiong Ng",
"Tat-Seng Chua"
] | 2311.00262 | 18,838 | https://openreview.net/forum?id=MCNqgUFTHI |
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[] | Poster | [] | Deep neural networks are susceptible to adversarial attacks, which can compromise their performance and accuracy. Adversarial Training (AT) has emerged as a popular approach for protecting neural networks against such attacks. However, a key challenge of AT is robust overfitting, where the network's robust performance on test data deteriorates with further training, thus hindering generalization. Motivated by the concept of active forgetting in the brain, we introduce a novel learning paradigm called "Forget to Mitigate Overfitting (FOMO)". FOMO alternates between the forgetting phase, which randomly forgets a subset of weights and regulates the model's information through weight reinitialization, and the relearning phase, which emphasizes learning generalizable features. Our experiments on benchmark datasets and adversarial attacks show that FOMO alleviates robust overfitting by significantly reducing the gap between the best and last robust test accuracy while improving the state-of-the-art robustness. Furthermore, FOMO provides a better trade-off between the standard and robust accuracy outperforming baseline adversarial methods. Finally, our framework is robust to AutoAttacks and increases generalization in many real-world scenarios. | [] | [] | The Effectiveness of Random Forgetting for Robust Generalization | [
"Vijaya Raghavan T Ramkumar",
"Bahram Zonooz",
"Elahe Arani"
] | 2402.11733 | 18,836 | https://openreview.net/forum?id=MEGQGNUfPx |
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[] | Poster | [] | Vision Transformers (ViTs) are now flourishing in the computer vision area. Despite the remarkable success, ViTs suffer from high computational cost, which greatly hinders their practical usage. Token reduction, which identifies and discards unimportant tokens during forward propagation, has then been proposed to make ViTs more efficient. For token reduction methodologies, a scoring metric is essential to distinguish between important and unimportant tokens. The attention score from the $\mathrm{[CLS]}$ token, which takes the responsibility to aggregate useful information and form the final output, has been established by prior works as an advantageous choice. Nevertheless, whereas the task pressure is applied at the end of the whole model, token reduction generally starts from very early blocks. Given the long distance in between, in the early blocks $\mathrm{[CLS]}$ token lacks the impetus to gather task-relevant information, causing somewhat arbitrary attention score allocation. This phenomenon, in turn, degrades the reliability of token scoring and substantially compromises the effectiveness of token reduction methods. Inspired by advances in the domain of dynamic neural networks, in this paper, we introduce Multi-Exit Token Reduction (METR), a simple romance between multi-exit architecture and token reduction—two areas previously considered orthogonal. By injecting early task pressure via multi-exit loss, the $\mathrm{[CLS]}$ token is spurred to collect task-related information in even early blocks, thus bolstering the credibility of $\mathrm{[CLS]}$ attention as a token-scoring metric. Additionally, we employ self-distillation to further refine the quality of early supervision. Extensive experiments substantiate both the existence and effectiveness of the newfound chemistry. Comparative assessments also indicate that METR outperforms state-of-the-art token reduction methods on standard benchmarks, especially under aggressive reduction ratio. Codes will be released. | [] | [] | A Simple Romance Between Multi-Exit Vision Transformer and Token Reduction | [
"Dongyang Liu",
"Meina Kan",
"Shiguang Shan",
"Xilin CHEN"
] | 18,147 | https://openreview.net/forum?id=gJeYtRuguR |
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[] | Spotlight Poster | [] | 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. | [] | [] | High-dimensional SGD aligns with emerging outlier eigenspaces | [
"Gerard Ben Arous",
"Reza Gheissari",
"Jiaoyang Huang",
"Aukosh Jagannath"
] | 2310.03010 | 18,834 | https://openreview.net/forum?id=MHjigVnI04 |
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[] | Poster | [] | We present Symphony, an $E(3)$ equivariant autoregressive generative model for 3D molecular geometriesthat iteratively builds a molecule from molecular fragments.Existing autoregressive models such as G-SchNet and G-SphereNet for molecules utilize rotationally invariant features to respect the 3D symmetries of molecules.In contrast, Symphony uses message-passing with higher-degree $E(3)$-equivariant features.This allows a novel representation of probability distributions via spherical harmonic signals to efficiently model the 3D geometry ofmolecules. We show that Symphony is able to accurately generate small molecules from the QM9 dataset, outperforming existingautoregressive models and approaching the performance of diffusion models. | [] | [] | Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation | [
"Ameya Daigavane",
"Song Eun Kim",
"Mario Geiger",
"Tess Smidt"
] | 2311.16199 | 18,833 | https://openreview.net/forum?id=MIEnYtlGyv |
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[] | Poster | [
"https://github.com/zkxufo/TTM"
] | As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature scaling is applied to both teacher's logits and student's logits in KD. Motivated by some recent works, in this paper, we drop instead temperature scaling on the student side, and systematically study the resulting variant of KD, dubbed transformed teacher matching (TTM). By reinterpreting temperature scaling as a power transform of probability distribution, we show that in comparison with the original KD, TTM has an inherent Rényi entropy term in its objective function, which serves as an extra regularization term. Extensive experiment results demonstrate that thanks to this inherent regularization, TTM leads to trained students with better generalization than the original KD. To further enhance student's capability to match teacher's power transformed probability distribution, we introduce a sample-adaptive weighting coefficient into TTM, yielding a novel distillation approach dubbed weighted TTM (WTTM). It is shown, by comprehensive experiments, that although WTTM is simple, it is effective, improves upon TTM, and achieves state-of-the-art accuracy performance. Our source code is available at https://github.com/zkxufo/TTM. | [] | [] | Knowledge Distillation Based on Transformed Teacher Matching | [
"Kaixiang Zheng",
"EN-HUI YANG"
] | 2402.11148 | 18,832 | https://openreview.net/forum?id=MJ3K7uDGGl |
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[] | Poster | [] | Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon manually designed algorithms, the parameterization of this learned objective function must be expressive enough to represent novel principles of learning (instead of merely recovering already established ones) while still generalizing to a wide range of settings outside of its meta-training distribution. However, existing methods focus on discovering objective functions that, like many widely used objective functions in reinforcement learning, do not take into account the total number of steps allowed for training, or “training horizon”. In contrast, humans use a plethora of different learning objectives across the course of acquiring a new ability. For instance, students may alter their studying techniques based on the proximity to exam deadlines and their self-assessed capabilities. This paper contends that ignoring the optimization time horizon significantly restricts the expressive potential of discovered learning algorithms. We propose a simple augmentation to two existing objective discovery approaches that allows the discovered algorithm to dynamically update its objective function throughout the agent’s training procedure, resulting in expressive schedules and increased generalization across different training horizons. In the process, we find that commonly used meta-gradient approaches fail to discover such adaptive objective functions while evolution strategies discover highly dynamic learning rules. We demonstrate the effectiveness of our approach on a wide range of tasks and analyze the resulting learned algorithms, which we find effectively balance exploration and exploitation by modifying the structure of their learning rules throughout the agent’s lifetime. | [] | [] | Discovering Temporally-Aware Reinforcement Learning Algorithms | [
"Matthew Thomas Jackson",
"Chris Lu",
"Louis Kirsch",
"Robert Tjarko Lange",
"Shimon Whiteson",
"Jakob Nicolaus Foerster"
] | 2402.05828 | 18,831 | https://openreview.net/forum?id=MJJcs3zbmi |
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[] | Poster | [
"https://github.com/wxie9/CARD"
] | Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-independent (CI) strategy to improve the training robustness. However, the ignorance of the correlation among different channels in CI would limit the model's forecasting capacity. In this work, we design a special Transformer, i.e., **C**hannel **A**ligned **R**obust Blen**d** Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals and dynamical dependence among multiple variables over time. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue. This new loss function weights the importance of forecasting over a finite horizon based on prediction uncertainties. Our evaluation of multiple long-term and short-term forecasting datasets demonstrates that CARD significantly outperforms state-of-the-art time series forecasting methods. The code is available at the following anonymous repository: https://anonymous.4open.science/r/CARD-6EEC | [] | [] | CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting | [
"Xue Wang",
"Tian Zhou",
"Qingsong Wen",
"Jinyang Gao",
"Bolin Ding",
"Rong Jin"
] | 2305.12095 | 18,830 | https://openreview.net/forum?id=MJksrOhurE |
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[] | Spotlight Poster | [] | This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation. | [] | [] | InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation | [
"Yi Wang",
"Yinan He",
"Yizhuo Li",
"Kunchang Li",
"Jiashuo Yu",
"Xin Ma",
"Xinhao Li",
"Guo Chen",
"Xinyuan Chen",
"Yaohui Wang",
"Ping Luo",
"Ziwei Liu",
"Yali Wang",
"Limin Wang",
"Yu Qiao"
] | 2307.06942 | 18,829 | https://openreview.net/forum?id=MLBdiWu4Fw |
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[] | Poster | [] | Recent works have shown that Large Language Models (LLMs) could empower traditional neuro-symbolic models via programming capabilities to translate lan- guages into module descriptions, thus achieving strong visual reasoning results while maintaining the model’s transparency and efficiency. However, these mod- els usually exhaustively generate the entire code snippet given each new instance of a task, which is extremely ineffective. On the contrary, human beings grad- ually acquire knowledge that can be reused and grow into more profound skills for fast generalization to new tasks since we are an infant. Inspired by this, we propose generative neuro-symbolic visual reasoning by growing and reusing mod- ules. Specifically, our model consists of three unique stages, module initialization, module generation, and module execution. First, given a vision-language task, we adopt LLMs to examine whether we could reuse and grow over established mod- ules to handle this new task. If not, we initialize a new module needed by the task and specify the inputs and outputs of this new module. After that, the new module is created by querying LLMs to generate corresponding code snippets that match the requirements. In order to get a better sense of the new module’s ability, we treat few-shot training examples as test cases to see if our new module could pass these cases. If yes, the new module is added to the module library for future reuse. Finally, we evaluate the performance of our model on the testing set by executing the parsed programs with the newly made visual modules to get the results. We find the proposed GNSVR model possesses several advantages. First, it performs competitively on standard tasks like visual question answering and referring ex- pression comprehension; Second, the visual modules learned from one task can be seamlessly transferred to new tasks; Last but not least, it is able to adapt to new visual reasoning tasks by observing a few training examples and reusing modules. | [] | [] | Generative Neuro-Symbolic Visual Reasoning by Growing and Reusing Modules | [
"Zhenfang Chen",
"Rui Sun",
"Wenjun Liu",
"Yining Hong",
"Chuang Gan"
] | 2311.04901 | 18,827 | https://openreview.net/forum?id=MNShbDSxKH |
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[] | Poster | [
"https://github.com/dmksjfl/SEABO"
] | Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment. Nevertheless, the success of offline RL relies heavily on the offline transitions annotated with reward labels. In practice, we often need to hand-craft the reward function, which is sometimes difficult, labor-intensive, or inefficient. To tackle this challenge, we set our focus on the offline imitation learning (IL) setting, and aim at getting a reward function based on the expert data and unlabeled data. To that end, we propose a simple yet effective search-based offline IL method, tagged SEABO. SEABO allocates a larger reward to the transition that is close to its closest neighbor in the expert demonstration, and a smaller reward otherwise, all in an unsupervised learning manner. Experimental results on a variety of D4RL datasets indicate that SEABO can achieve competitive performance to offline RL algorithms with ground-truth rewards, given only a single expert trajectory, and can outperform prior reward learning and offline IL methods across many tasks. Moreover, we demonstrate that SEABO also works well if the expert demonstrations contain only observations. Our code is publicly available at https://github.com/dmksjfl/SEABO. | [] | [] | SEABO: A Simple Search-Based Method for Offline Imitation Learning | [
"Jiafei Lyu",
"Xiaoteng Ma",
"Le Wan",
"Runze Liu",
"Xiu Li",
"Zongqing Lu"
] | 2402.03807 | 18,826 | https://openreview.net/forum?id=MNyOI3C7YB |
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[] | Spotlight Poster | [] | Most interpretability research in NLP focuses on understanding the behavior and features of a fully trained model. However, certain insights into model behavior may only be accessible by observing the trajectory of the training process. We present a case study of syntax acquisition in masked language models (MLMs) that demonstrates how analyzing the evolution of interpretable artifacts throughout training deepens our understanding of emergent behavior. In particular, we study Syntactic Attention Structure (SAS), a naturally emerging property of MLMs wherein specific Transformer heads tend to focus on specific syntactic relations. We identify a brief window in pretraining when models abruptly acquire SAS, concurrent with a steep drop in loss. This breakthrough precipitates the subsequent acquisition of linguistic capabilities. We then examine the causal role of SAS by manipulating SAS during training, and demonstrate that SAS is necessary for the development of grammatical capabilities. We further find that SAS competes with other beneficial traits during training, and that briefly suppressing SAS improves model quality. These findings offer an interpretation of a real-world example of both simplicity bias and breakthrough training dynamics. | [] | [] | Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs | [
"Angelica Chen",
"Ravid Shwartz-Ziv",
"Kyunghyun Cho",
"Matthew L Leavitt",
"Naomi Saphra"
] | 2309.07311 | 18,825 | https://openreview.net/forum?id=MO5PiKHELW |
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[] | Poster | [] | Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we address this issue by learning the inherent structure of action-wise similar MDP to appropriately balance the performance degradation versus sample/computational complexity. In particular, we partition the action spaces into multiple groups based on the similarity in transition distribution and reward function, and build a linear decomposition model to capture the difference between the intra-group transition kernel and the intra-group rewards. Both our theoretical analysis and experiments reveal a *surprising and counter-intuitive result*: while a more refined grouping strategy can reduce the approximation error caused by treating actions in the same group as identical, it also leads to increased estimation error when the size of samples or the computation resources is limited. This finding highlights the grouping strategy as a new degree of freedom that can be optimized to minimize the overall performance loss. To address this issue, we formulate a general optimization problem for determining the optimal grouping strategy, which strikes a balance between performance loss and sample/computational complexity. We further propose a computationally efficient method for selecting a nearly-optimal grouping strategy, which maintains its computational complexity independent of the size of the action space. | [] | [] | Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping | [
"Yining Li",
"Peizhong Ju",
"Ness Shroff"
] | 2306.12981 | 18,823 | https://openreview.net/forum?id=MOmqfJovQ6 |
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[] | Poster | [] | Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits. Inferred neural interactions from neural signals primarily reflect functional interactions. In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states, and hence functional interactions can change over time. To model dynamically changing functional interactions, prior work employs state-switching generalized linear models with hidden Markov models (i.e., HMM-GLMs). However, we argue they lack biological plausibility, as functional interactions are shaped and confined by the underlying anatomical connectome. Here, we propose a novel prior-informed state-switching GLM. We introduce both a Gaussian prior and a one-hot prior over the GLM in each state. The priors are learnable. We will show that the learned prior should capture the state-constant interaction, shedding light on the underlying anatomical connectome and revealing more likely physical neuron interactions. The state-dependent interaction modeled by each GLM offers traceability to capture functional variations across multiple brain states. Our methods effectively recover true interaction structures in simulated data, achieve the highest predictive likelihood with real neural datasets, and render interaction structures and hidden states more interpretable when applied to real neural data. | [] | [] | One-hot Generalized Linear Model for Switching Brain State Discovery | [
"Chengrui Li",
"Soon Ho Kim",
"Chris Rodgers",
"Hannah Choi",
"Anqi Wu"
] | 2310.15263 | 18,822 | https://openreview.net/forum?id=MREQ0k6qvD |
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[] | Poster | [] | Modular approaches that use a different composition of modules for each problem are a promising direction in continual learning (CL). However, searching through the large, discrete space of module compositions is challenging, especially because evaluating a composition’s performance requires a round of neural network training. We address this challenge through a modular CL framework, PICLE, that uses a probabilistic model to cheaply compute the fitness of each composition, allowing PICLE to achieve both perceptual, few-shot and latent transfer. The model combines prior knowledge about good module compositions with dataset-specific information. We evaluate PICLE using two benchmark suites designed to assess different desiderata of CL techniques. Comparing to a wide range of approaches, we show that PICLE is the first modular CL algorithm to achieve perceptual, few-shot and latent transfer while scaling well to large search spaces, outperforming previous state-of-the-art modular CL approaches on long problem sequences. | [] | [] | A Probabilistic Framework for Modular Continual Learning | [
"Lazar Valkov",
"Akash Srivastava",
"Swarat Chaudhuri",
"Charles Sutton"
] | 2306.06545 | 18,820 | https://openreview.net/forum?id=MVe2dnWPCu |
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[] | Poster | [] | Safety lies at the core of the development of Large Language Models (LLMs). There is ample work on aligning LLMs with human ethics and preferences, including data filtering in pretraining, supervised fine-tuning, reinforcement learning from human feedback, red teaming, etc. In this study, we discover that chat in cipher can bypass the safety alignment techniques of LLMs, which are mainly conducted in natural languages. We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers. CipherChat enables humans to chat with LLMs through cipher prompts topped with system role descriptions and few-shot enciphered demonstrations. We use CipherChat to assess state-of-the-art LLMs, including ChatGPT and GPT-4 for different representative human ciphers across 11 safety domains in both English and Chinese. Experimental results show that certain ciphers succeed almost 100% of the time in bypassing the safety alignment of GPT-4 in several safety domains, demonstrating the necessity of developing safety alignment for non-natural languages. Notably, we identify that LLMs seem to have a ''secret cipher'', and propose a novel SelfCipher that uses only role play and several unsafe demonstrations in natural language to evoke this capability. SelfCipher surprisingly outperforms existing human ciphers in almost all cases. | [] | [] | GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher | [
"Youliang Yuan",
"Wenxiang Jiao",
"Wenxuan Wang",
"Jen-tse Huang",
"Pinjia He",
"Shuming Shi",
"Zhaopeng Tu"
] | 18,817 | https://openreview.net/forum?id=MbfAK4s61A |
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[] | Poster | [] | Modern solvers for solving mixed integer programming (MIP) often rely on the branch-and-bound (B&B) algorithm which could be of high time complexity, and presolving techniques are well designed to simplify the instance as pre-processing before B&B. However, such presolvers in existing literature or open-source solvers are mostly set by default agnostic to specific input instances, and few studies have been reported on tailoring presolving settings. In this paper, we aim to dive into this open question and show that the MIP solver can be indeed largely improved when switching the default instance-agnostic presolving into instance-specific presolving. Specifically, we propose a combination of supervised learning and classic heuristics to achieve efficient presolving adjusting, avoiding tedious reinforcement learning. Notably, our approach is orthogonal from many recent efforts in incorporating learning modules into the B&B framework after the presolving stage, and to our best knowledge, this is the first work for introducing learning to presolve in MIP solvers. Experiments on multiple real-world datasets show that well-trained neural networks can infer proper presolving for arbitrary incoming MIP instances in less than 0.5s, which is neglectable compared with the solving time often hours or days. We plan to open-source our code as a benchmark for this new task, and currently, the code is available at an anonymous repository. | [] | [] | L2P-MIP: Learning to Presolve for Mixed Integer Programming | [
"Chang Liu",
"Zhichen Dong",
"Haobo Ma",
"Weilin Luo",
"Xijun Li",
"Bowen Pang",
"Jia Zeng",
"Junchi Yan"
] | 18,816 | https://openreview.net/forum?id=McfYbKnpT8 |
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[] | Poster | [] | Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could neuronal operations solve this problem is an important question for AI and neuroscience. Many previous studies draw inspiration from observed neuroscience phenomena and propose episodic replay or synaptic metaplasticity, but they are not guaranteed to explicitly preserve knowledge for neuron populations. Other works focus on machine learning methods with more mathematical grounding, e.g., orthogonal projection on high dimensional spaces, but there is no neural correspondence for neuromorphic computing. In this work, we develop a new method with neuronal operations based on lateral connections and Hebbian learning, which can protect knowledge by projecting activity traces of neurons into an orthogonal subspace so that synaptic weight update will not interfere with old tasks. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities and enable orthogonal projection. This provides new insights into how neural circuits and Hebbian learning can help continual learning, and also how the concept of orthogonal projection can be realized in neuronal systems. Our method is also flexible to utilize arbitrary training methods based on presynaptic activities/traces. Experiments show that our method consistently solves forgetting for spiking neural networks with nearly zero forgetting under various supervised training methods with different error propagation approaches, and outperforms previous approaches under various settings. Our method can pave a solid path for building continual neuromorphic computing systems. The code is available at https://github.com/pkuxmq/HLOP-SNN. | [] | [] | Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks | [
"Mingqing Xiao",
"Qingyan Meng",
"Zongpeng Zhang",
"Di He",
"Zhouchen Lin"
] | 2402.11984 | 18,815 | https://openreview.net/forum?id=MeB86edZ1P |
|
[] | Poster | [] | Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for desirable generations. A notable subject that is understudied is the (in-)consistency of RMs --- whether they can recognize the semantic changes to different prompts and appropriately adapt their reward assignments--- and their impact on the downstream RLHF model.In this paper, we visit a series of research questions relevant to RM inconsistency:(1) How can we measure the consistency of reward models? (2) How consistent are the existing RMs and how can we improve them? (3) In what ways does reward inconsistency influence the chatbots resulting from the RLHF model training?We propose **Contrast Instruction** -- a benchmarking strategy for the consistency of RM. Each example in **Contrast Instruction** features a pair of lexically similar instructions with different ground truth responses. A consistent RM is expected to rank the corresponding instruction and response higher than other combinations. We observe that current RMs trained with the standard ranking objective fail miserably on \contrast{} compared to average humans. To show that RM consistency can be improved efficiently without using extra training budget, we propose two techniques **ConvexDA** and **RewardFusion**, which enhance reward consistency through extrapolation during the RM training and inference stage, respectively.We show that RLHF models trained with a more consistent RM yield more useful responses, suggesting that reward inconsistency exhibits a trickle-down effect on the downstream RLHF process. | [] | [] | The Trickle-down Impact of Reward Inconsistency on RLHF | [
"Lingfeng Shen",
"Sihao Chen",
"Linfeng Song",
"Lifeng Jin",
"Baolin Peng",
"Haitao Mi",
"Daniel Khashabi",
"Dong Yu"
] | 18,814 | https://openreview.net/forum?id=MeHmwCDifc |
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[] | Poster | [] | Deep Ensemble approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging towards various local optima. However, a limitation of this methodology lies in its high computational overhead for inference, arising from the necessity to store numerous learned parameters and execute individual forward passes for each parameter during the inference stage.We propose a novel approach called Diffusion Bridge Network to address this challenge. Based on the theory of Schr\"odinger bridge, this method directly learns to simulate an Stochastic Differential Equation (SDE) that connects the output distribution of a single ensemble member to the output distribution of the ensembled model, allowing us to obtain ensemble prediction without having to invoke forward pass through all the ensemble models. By substituting the heavy ensembles with this lightweight neural network constructing DBN, we achieved inference with reduced computational cost while maintaining accuracy and uncertainty scores on benchmark datasets such as CIFAR-10, CIFAR-100, and TinyImageNet. | [] | [] | Fast Ensembling with Diffusion Schrödinger Bridge | [
"Hyunsu Kim",
"Jongmin Yoon",
"Juho Lee"
] | 18,813 | https://openreview.net/forum?id=Mgq6kxl115 |
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[] | Spotlight Poster | [] | 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. | [] | [] | Learning to Act from Actionless Videos through Dense Correspondences | [
"Po-Chen Ko",
"Jiayuan Mao",
"Yilun Du",
"Shao-Hua Sun",
"Joshua B. Tenenbaum"
] | 2310.08576 | 18,812 | https://openreview.net/forum?id=Mhb5fpA1T0 |
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[] | Poster | [] | Evaluating deep neural networks (DNNs) as models of human perception has given rich insights into both human visual processing and representational properties of DNNs. We extend this work by analyzing how well DNNs perform compared to humans when constrained by peripheral vision -- which limits human performance on a variety of tasks, but also benefits the visual system significantly. We evaluate this by (1) modifying the Texture Tiling Model (TTM), a well tested model of peripheral vision to be more flexibly used with DNNs, (2) generating a large dataset which we call COCO-Periph that contains images transformed to capture the information available in human peripheral vision, and (3) comparing DNNs to humans at peripheral object detection using a psychophysics experiment. Our results show that common DNNs underperform at object detection compared to humans when simulating peripheral vision with TTM. Training on COCO-Periph begins to reduce the gap between human and DNN performance and leads to small increases in corruption robustness, but DNNs still struggle to capture human-like sensitivity to peripheral clutter. Our work brings us closer to accurately modeling human vision, and paves the way for DNNs to mimic and sometimes benefit from properties of human visual processing. | [] | [] | COCO-Periph: Bridging the Gap Between Human and Machine Perception in the Periphery | [
"Anne Harrington",
"Vasha DuTell",
"Mark Hamilton",
"Ayush Tewari",
"Simon Stent",
"William T. Freeman",
"Ruth Rosenholtz"
] | 18,811 | https://openreview.net/forum?id=MiRPBbQNHv |
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[] | Poster | [
"https://github.com/sbb-gh/experimental-design-multichannel"
] | This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current standard approaches in experimental design focus on model-parameter estimation and require specification of a particular model, whereas in imaging, other tasks may drive the design. Furthermore, such approaches are often lead to intractable optimisation problems in real-world imaging applications. Here we put forward a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. The approach obtains data densely-sampled over the measurement space (many image channels) for a small number of acquisitions, then identifies a subset of channels of pre-specified size that best supports the task. We propose a method: TADRED for TAsk-DRiven experimental design in imaging, to identify the most informative channel-subset whilst simultaneously training a network to execute the task given the subset. Experiments demonstrate the potential of TADRED in diverse imaging applications: several clinically-relevant tasks in magnetic resonance imaging; and remote sensing and physiological applications of hyperspectral imaging. Results show substantial improvement over classical experimental design, two recent application-specific methods within the new paradigm we explore, and state-of-the-art approaches in supervised feature selection. We anticipate further applications of our approach; code (for reviewers) is available: \cite{ouranonymouscode}. | [] | [] | Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection | [
"Stefano B. Blumberg",
"Paddy J. Slator",
"Daniel C. Alexander"
] | 2210.06891 | 18,810 | https://openreview.net/forum?id=MloaGA6WwX |
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[] | Spotlight Poster | [] | Visual understanding of our world goes beyond the semantics and flat structure of individual images.In this paper, we work towards capturing both the 3D structure as well as the dynamics of real-world scenes from monocular real-world videos.Our model, the Dynamic Scene Transformer (DyST), builds upon recent work in neural scene representation and learns a latent decomposition into scene content as well as per-view scene dynamics and camera pose. This separation is achieved through a special co-training scheme on monocular videos and our new synthetic dataset DySO.DyST learns tangible latent representations for dynamic scenes that enable view generation with separate control over the camera and the content of the scene. | [] | [] | DyST: Towards Dynamic Neural Scene Representations on Real-World Videos | [
"Maximilian Seitzer",
"Sjoerd van Steenkiste",
"Thomas Kipf",
"Klaus Greff",
"Mehdi S. M. Sajjadi"
] | 2310.06020 | 18,809 | https://openreview.net/forum?id=MnMWa94t12 |
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[] | Poster | [] | Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym, and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://dittogym.github.io. | [] | [] | DittoGym: Learning to Control Soft Shape-Shifting Robots | [
"Suning Huang",
"Boyuan Chen",
"Huazhe Xu",
"Vincent Sitzmann"
] | 2401.13231 | 18,808 | https://openreview.net/forum?id=MpyFAhH9CK |
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[] | Spotlight Poster | [] | Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive. This paper investigates the memorization abilities of multi-head attention mechanisms, examining how many example sequences they can memorize, as a function of the number of heads and sequence length. Motivated by experimental findings on vision transformers, we introduce novel assumptions about the linear independence of input data, distinct from the commonly used general-position assumption. Under these assumptions, we demonstrate that an attention layer with $H$ heads, dimension $d$, and context size $n < d,$ featuring $\Theta(Hd^2)$ parameters, can memorize $\Omega(Hn)$ examples. Our analysis sheds light on how different attention heads handle various example sequences, aided by the softmax operator’s saturation property. We validate our findings through experiments on synthetic data. | [] | [] | Memorization Capacity of Multi-Head Attention in Transformers | [
"Sadegh Mahdavi",
"Renjie Liao",
"Christos Thrampoulidis"
] | 2306.02010 | 18,807 | https://openreview.net/forum?id=MrR3rMxqqv |
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[] | Poster | [] | We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones. At the core of PeFLL lies a learning-to-learn approach that jointly trains an embedding network and a hypernetwork. The embedding network is used to represent clients in a latent descriptor space in a way that reflects their similarity to each other. The hypernetwork takes as input such descriptors and outputs the parameters of fully personalized client models. In combination, both networks constitute a learning algorithm that achieves state-of-the-art performance in several personalized federated learning benchmarks. | [] | [] | PeFLL: Personalized Federated Learning by Learning to Learn | [
"Jonathan Scott",
"Hossein Zakerinia",
"Christoph H Lampert"
] | 2306.05515 | 18,806 | https://openreview.net/forum?id=MrYiwlDRQO |
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[] | Poster | [] | Most traditional causal discovery approaches typically assume the absence of latent variables, a simplification that often does not align with real-world situations. Recently, there has been a surge of causal discovery methods that explicitly consider latent variables. While causal discovery with latent variables aims to reveal causal relations between observed variables in the presence of latent variables, latent causal structure learning seeks to identify latent variables and infer their causal relations, typically entailing strong distributional and graphical assumptions. In this paper, we endeavor to recover the whole causal structure involving both latent and observed variables under relatively milder assumptions. Specifically, we introduce two sets of assumptions, one allows arbitrary distribution and requires only one pure child per latent variable, the other requires no pure child and imposes the non-Gaussianity requirement on only a small subset of variables, and both of them allow causal edges between observed variables. Under either of them, we prove that the whole causal structure of linear latent variable models is identifiable. Our proof is constructive, leading to both theoretically sound and computationally efficient algorithms, which first identify latent variables from observed data and then infer causal relations between any two variables. | [] | [] | Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions | [
"Xiu-Chuan Li",
"Kun Zhang",
"Tongliang Liu"
] | 18,805 | https://openreview.net/forum?id=MukGKGtgnr |
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[] | Poster | [] | "Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first address compelling challenges related to the ``forward-only" rules, which include reducing the performance gap with backpropagation and providing an analytical understanding of their dynamics. To this end, we show that the forward-only algorithm with top-down feedback is well-approximated by an "adaptive-feedback-alignment" algorithm and we analytically track its performance during learning in a prototype high-dimensional setting. Then, we compare different versions of forward-only algorithms, focusing on the Forward-Forward and PEPITA frameworks, and we show that they share the same principles. Overall, our work unveils the connections between three key neuro-inspired learning rules, providing a link between "forward-only" algorithms, i.e., Forward-Forward and PEPITA, and an approximation of backpropagation, i.e., Feedback Alignment. | [] | [] | Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization | [
"Ravi Francesco Srinivasan",
"Francesca Mignacco",
"Martino Sorbaro",
"Maria Refinetti",
"Avi Cooper",
"Gabriel Kreiman",
"Giorgia Dellaferrera"
] | 2302.05440 | 18,804 | https://openreview.net/forum?id=My7lkRNnL9 |
|
[] | Poster | [] | We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision.To resolve this, we propose to align user interests with multi-faceted item characteristics. First, we involve prototype-based representation learning to discover item characteristics along multiple facets. Second, we compose user interests from uncovered item characteristics via binding mechanism, separating the granularity of user preferences from that of item space. Third, we design a dedicated bi-directional binding block, aiding the derivation of compositional user interests.On real-world datasets, the experimental results demonstrate the strong performance of our proposed method compared to a series of baselines. | [] | [] | Learning Multi-Faceted Prototypical User Interests | [
"Nhu-Thuat Tran",
"Hady W. Lauw"
] | 18,803 | https://openreview.net/forum?id=MzjiMxlWab |
||
[] | Poster | [] | Reinforcement learning (RL) requires either manually specifying a reward function, which is often infeasible, or learning a reward model from a large amount of human feedback, which is often very expensive. We study a more sampleefficient alternative: using pretrained vision-language models (VLMs) as zeroshot reward models (RMs) to specify tasks via natural language. We propose a natural and general approach to using VLMs as reward models, which we call VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn complex tasks without a manually specified reward function, such as kneeling, doing the splits, and sitting in a lotus position. For each of these tasks, we only provide a single sentence text prompt describing the desired task with minimal prompt engineering. We provide videos of the trained agents at: https://sites.google.com/view/anon-vlmrm. We can improve performance by providing a second “baseline” prompt and projecting out parts of the CLIP embedding space irrelevant to distinguish between goal and baseline. Further, we find a strong scaling effect for VLM-RMs: larger VLMs trained with more compute and data are better reward models. The failure modes of VLM-RMs we encountered are all related to known capability limitations of current VLMs, such as limited spatial reasoning ability or visually unrealistic environments that are far off-distribution for the VLM. We find that VLM-RMs are remarkably robust as long as the VLM is large enough. This suggests that future VLMs will become more and more useful reward models for a wide range of RL applications. | [] | [] | Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning | [
"Juan Rocamonde",
"Victoriano Montesinos",
"Elvis Nava",
"Ethan Perez",
"David Lindner"
] | 2310.12921 | 18,802 | https://openreview.net/forum?id=N0I2RtD8je |
|
[] | Poster | [] | Given a matrix $M\in \mathbb{R}^{m\times n}$, the low rank matrix completion problem asks us to find a rank-$k$ approximation of $M$ as $UV^\top$ for $U\in \mathbb{R}^{m\times k}$ and $V\in \mathbb{R}^{n\times k}$ by only observing a few entries specified by a set of entries $\Omega\subseteq [m]\times [n]$. In particular, we examine an approach that is widely used in practice --- the alternating minimization framework. Jain, Netrapalli and Sanghavi showed that if $M$ has incoherent rows and columns, then alternating minimization provably recovers the matrix $M$ by observing a nearly linear in $n$ number of entries. While the sample complexity has been subsequently improved, alternating minimization steps are required to be computed exactly. This hinders the development of more efficient algorithms and fails to depict the practical implementation of alternating minimization, where the updates are usually performed approximately in favor of efficiency.In this paper, we take a major step towards a more efficient and error-robust alternating minimization framework. To this end, we develop an analytical framework for alternating minimization that can tolerate moderate amount of errors caused by approximate updates. Moreover, our algorithm runs in time $\widetilde O(|\Omega| k)$, which is nearly linear in the time to verify the solution while preserving the sample complexity. This improves upon all prior known alternating minimization approaches which require $\widetilde O(|\Omega| k^2)$ time. | [] | [] | Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time | [
"Yuzhou Gu",
"Zhao Song",
"Junze Yin",
"Lichen Zhang"
] | 2302.11068 | 18,801 | https://openreview.net/forum?id=N0gT4A0jNV |
|
[] | Poster | [
"https://github.com/HyunWookL/TESTAM"
] | Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph. By introducing different experts and properly routing them, TESTAM could better model various circumstances, including spatially isolated nodes, highly related nodes, and recurring and non-recurring events. For the proper routing, we reformulate a gating problem into a classification problem with pseudo labels. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM achieves a better indication and modeling of recurring and non-recurring traffic. | [] | [] | TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts | [
"Hyunwook Lee",
"Sungahn Ko"
] | 2403.02600 | 18,800 | https://openreview.net/forum?id=N0nTk5BSvO |
|
[] | Poster | [] | Transformers have become the standard in state-of-the-art vision architectures, achieving impressive performance on both image-level and dense pixelwise tasks. However, training vision transformers for high-resolution pixelwise tasks has a prohibitive cost. Typical solutions boil down to hierarchical architectures, fast and approximate attention, or training on low-resolution crops. This latter solution does not constrain architectural choices, but it leads to a clear performance drop when testing at resolutions significantly higher than that used for training, thus requiring ad-hoc and slow post-processing schemes. In this paper, we propose a novel strategy for efficient training and inference of high-resolution vision transformers: the key principle is to mask out most of the high-resolution inputs during training, keeping only N random windows. This allows the model to learn local interactions between tokens inside each window, and global interactions between tokens from different windows. As a result, the model can directly process the high-resolution input at test time without any special trick. We show that this strategy is effective when using relative positional embedding such as rotary embeddings. It is 4 times faster to train than a full-resolution network, and it is straightforward to use at test time compared to existing approaches. We apply this strategy to the dense monocular task of semantic segmentation, and find that a simple setting with 2 windows performs best, hence the name of our method: Win-Win. To demonstrate the generality of our contribution, we further extend it to the binocular task of optical flow, reaching state-of-the-art performance on the Spring benchmark that contains Full-HD images with an inference time an orderof magnitude faster than the best competitor. | [] | [] | Win-Win: Training High-Resolution Vision Transformers from Two Windows | [
"Vincent Leroy",
"Jerome Revaud",
"Thomas Lucas",
"Philippe Weinzaepfel"
] | 18,799 | https://openreview.net/forum?id=N23A4ybMJr |
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[] | Poster | [] | In this study, we aim to enhance the arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization and elucidate two ensuing challenges that impede the successful and economical design of prompt optimization techniques. One primary issue is the absence of an effective method to evaluate prompts during inference when the golden answer is unavailable. Concurrently, learning via interactions with the LLMs to navigate the expansive natural language prompting space proves to be resource-intensive.To address this, we introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data. Such data exists as by-products when diverse prompts are benchmarked on open-accessible datasets. With Prompt-OIRL, the query-dependent prompt optimization objective is achieved by first learning an offline reward model. This model can evaluate any query-prompt pairs without accessing LLMs. Subsequently, a best-of-N strategy is deployed to recommend the optimal prompt. Our experimental evaluations across various LLM scales and arithmetic reasoning datasets underscore both the efficacy and economic viability of the proposed approach. | [] | [] | Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL | [
"Hao Sun",
"Alihan Hüyük",
"Mihaela van der Schaar"
] | 2309.06553 | 18,797 | https://openreview.net/forum?id=N6o0ZtPzTg |
|
[] | Spotlight Poster | [] | Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (\eg, LLaMA-2) are still far away from satisfactory for solving mathematical problems due to the complex reasoning procedures. To bridge this gap, we propose \emph{MetaMath}, a finetuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives, which results in a new dataset called {MetaMathQA}. Then we finetune the LLaMA-2 models on MetaMathQA. Experimental results on two popular benchmarks (\ie, GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a significant margin. Our MetaMath-7B model achieves $66.5\%$ on GSM8K and $19.8\%$ on MATH, exceeding the state-of-the-art models of the same size by $11.5\%$ and $8.7\%$. Particularly, MetaMath-70B achieves an accuracy of $82.3\%$ on GSM8K, slightly better than GPT-3.5-Turbo. We release the MetaMathQA dataset, the MetaMath models with different model sizes and the training code for public use. | [] | [] | MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models | [
"Longhui Yu",
"Weisen Jiang",
"Han Shi",
"Jincheng YU",
"Zhengying Liu",
"Yu Zhang",
"James Kwok",
"Zhenguo Li",
"Adrian Weller",
"Weiyang Liu"
] | 2309.12284 | 18,796 | https://openreview.net/forum?id=N8N0hgNDRt |
|
[] | Poster | [] | We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accurate inductive biases from the limited scale of training data. In particular, the queries often attend to points that are far away from the target objects, violating the locality principle in object detection. To address the limitation, we introduce a novel 3D Vertex Relative Position Encoding (3DV-RPE) method which computes position encoding for each point based on its relative position to the 3D boxes predicted by the queries in each decoder layer, thus providing clear information to guide the model to focus on points near the objects, in accordance with the principle of locality. Furthermore, we have systematically refined our pipeline, including data normalization, to better align with the task requirements. Our approach demonstrates remarkable performance on the demanding ScanNetV2 benchmark, showcasing substantial enhancements over the prior state-of-the-art CAGroup3D. Specifically, we achieve an increase in $AP_{25}$ from $75.1\%$ to $77.8\%$ and in ${AP}_{50}$ from $61.3\%$ to $66.0\%$, all while achieving a nearly $2\times$ speed improvement during inference. | [] | [] | V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection | [
"Yichao Shen",
"Zigang Geng",
"Yuhui Yuan",
"Yutong Lin",
"Ze Liu",
"Chunyu Wang",
"Han Hu",
"Nanning Zheng",
"Baining Guo"
] | 18,795 | https://openreview.net/forum?id=NDkpxG94sF |
||
[] | Poster | [] | Recent works have introduced LEAPS and HPRL, systems that learn latent spaces of domain-specific languages, which are used to define programmatic policies for partially observable Markov decision processes (POMDPs). These systems induce a latent space while optimizing losses such as the behavior loss, which aim to achieve locality in program behavior, meaning that vectors close in the latent space should correspond to similarly behaving programs. In this paper, we show that the programmatic space, induced by the domain-specific language and requiring no training, presents values for the behavior loss similar to those observed in latent spaces presented in previous work. Moreover, algorithms searching in the programmatic space significantly outperform those in LEAPS and HPRL. To explain our results, we measured the ``friendliness'' of the two spaces to local search algorithms. We discovered that algorithms are more likely to stop at local maxima when searching in the latent space than when searching in the programmatic space. This implies that the optimization topology of the programmatic space, induced by the reward function in conjunction with the neighborhood function, is more conducive to search than that of the latent space. This result provides an explanation for the superior performance in the programmatic space. | [] | [] | Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces | [
"Tales Henrique Carvalho",
"Kenneth Tjhia",
"Levi Lelis"
] | 18,793 | https://openreview.net/forum?id=NGVljI6HkR |
||
[] | Poster | [] | Recently, hypergraph neural networks (HGNNs) exhibit the potential to tackle tasks with high-order correlations and have achieved success in many tasks. However, existing evolution on the hypergraph has poor controllability and lacks sufficient theoretical support (like dynamic systems), thus yielding sub-optimal performance.One typical scenario is that only one or two layers of HGNNs can achieve good results and more layers lead to degeneration of performance.Under such circumstances, it is important to increase the controllability of HGNNs.In this paper, we first introduce hypergraph dynamic systems (HDS), which bridge hypergraphs and dynamic systems and characterize the continuous dynamics of representations.We then propose a control-diffusion hypergraph dynamic system by an ordinary differential equation (ODE).We design a multi-layer HDS$^{ode}$ as a neural implementation, which contains control steps and diffusion steps.HDS$^{ode}$ has the properties of controllability and stabilization and is allowed to capture long-range correlations among vertices.Experiments on $7$ datasets demonstrate HDS$^{ode}$ beat all compared methods.HDS$^{ode}$ achieves stable performance with increased layers and solves the poor controllability of HGNNs.We also provide the feature visualization of the evolutionary process to demonstrate the controllability and stabilization of HDS$^{ode}$. | [] | [] | Hypergraph Dynamic System | [
"Jielong Yan",
"Yifan Feng",
"Shihui Ying",
"Yue Gao"
] | 18,791 | https://openreview.net/forum?id=NLbRvr840Q |
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[] | Spotlight Poster | [] | Contrastive learning is a highly successful technique for learning representations of data from labeled tuples, specifying the distance relations within the tuple. We study the sample complexity of contrastive learning, i.e. the minimum number of labeled tuples sufficient for getting high generalization accuracy. We give tight bounds on the sample complexity in a variety of settings, focusing on arbitrary distance functions, $\ell_p$-distances, and tree metrics. Our main result is an (almost) optimal bound on the sample complexity of learning $\ell_p$-distances for integer $p$. For any $p \ge 1$, we show that $\tilde \Theta(nd)$ labeled tuples are necessary and sufficient for learning $d$-dimensional representations of $n$-point datasets. Our results hold for an arbitrary distribution of the input samples and are based on giving the corresponding bounds on the Vapnik-Chervonenkis/Natarajan dimension of the associated problems. We further show that the theoretical bounds on sample complexity obtained via VC/Natarajan dimension can have strong predictive power for experimental results, in contrast with the folklore belief about a substantial gap between the statistical learning theory and the practice of deep learning. | [] | [] | Optimal Sample Complexity of Contrastive Learning | [
"Noga Alon",
"Dmitrii Avdiukhin",
"Dor Elboim",
"Orr Fischer",
"Grigory Yaroslavtsev"
] | 2312.00379 | 18,786 | https://openreview.net/forum?id=NU9AYHJvYe |
|
[] | Poster | [] | Diffusion models have the ability to produce high-quality images with remarkable realism and diversity. Their effectiveness heavily relies on massive training on large-scale datasets, which, however, can be considerably impaired in the presence of real-world long-tail data. For long tail diffusion model generation, current works focus on the calibration and enhancement of the tail generation with head-tail knowledge transfer. The transfer process relies on the abundant diversity derived from the head class and, more significantly, the condition capacity of the model prediction. However, it is worth noting that the dependency on the conditional model prediction to realize the knowledge transfer might exhibit bias during training, leading to unsatisfactory generation results and lack of robustness. To address the issue, we directly establish the knowledge transfer from head data samples, based on the multi-objective characteristics of the score function in the diffusion process. To this end, a directional calibration for the estimation of noisy tail sample score is performed towards the clean head samples~(T2H), leveraging the similarity within the data distribution from head to tail classes. This augmentation for the tail score estimation encourages better diversity in generating the samples of tail categories. We extensively evaluate our approach with experiments on multiple benchmark datasets, demonstrating its effectiveness and superior performance compared to existing methods. | [] | [] | Long-tailed Diffusion Models with Oriented Calibration | [
"Tianjiao Zhang",
"Huangjie Zheng",
"Jiangchao Yao",
"Xiangfeng Wang",
"Mingyuan Zhou",
"Ya Zhang",
"Yanfeng Wang"
] | 18,785 | https://openreview.net/forum?id=NW2s5XXwXU |
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[] | Poster | [] | Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons. Existing prompt learning techniques replace hand-crafted text prompts with learned vectors that offer improvements on seen classes but struggle to generalize to unseen classes. Our work addresses this challenge by proposing Federated Text-driven Prompt Generation (FedTPG), which learns a unified prompt generation network across multiple remote clients in a scalable manner. The prompt generation network is conditioned on task-related text input, thus, is context-aware, making it suitable to generalize for both seen and unseen classes. Our comprehensive empirical evaluations on nine diverse image classification datasets show that our method is superior to existing federated prompt learning methods, that achieve overall better generalization on both seen and unseen classes and is also generalizable to unseen datasets. | [] | [] | Federated Text-driven Prompt Generation for Vision-Language Models | [
"Chen Qiu",
"Xingyu Li",
"Chaithanya Kumar Mummadi",
"Madan Ravi Ganesh",
"Zhenzhen Li",
"Lu Peng",
"Wan-Yi Lin"
] | 2310.06123 | 18,784 | https://openreview.net/forum?id=NW31gAylIm |
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[] | Poster | [] | In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse effects by correcting models. In this paper, we seek to break this inherent property by generating data to complement the original dataset to fundamentally mitigate heterogeneity level. As a novel attempt from the perspective of data, we propose federated learning with consensus-oriented generation (FedCOG). FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset.FedCOG has two critical advantages: 1) it can be a plug-and-play module to further improve the performance of most existing FL methods, and 2) it is naturally compatible with standard FL protocols such as Secure Aggregation since it makes no modification in communication process.Extensive experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods and has the plug-and-play property. | [] | [] | Fake It Till Make It: Federated Learning with Consensus-Oriented Generation | [
"Rui Ye",
"Yaxin Du",
"Zhenyang Ni",
"Yanfeng Wang",
"Siheng Chen"
] | 2312.05966 | 18,783 | https://openreview.net/forum?id=NY3wMJuaLf |
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[] | Poster | [] | Branch-and-bound (B\&B) has long been favored for tackling complex Mixed Integer Programming (MIP) problems, where the choice of branching strategy plays a pivotal role. Recently, Imitation Learning (IL)-based policies have emerged as potent alternatives to traditional rule-based approaches. However, it is nontrivial of acquiring high-quality training samples, and IL often converges to suboptimal variable choices for branching, restricting the overall performance. In response to these challenges, we propose a novel hybrid online and offline reinforcement learning (RL) approach to enhance the branching policy by cost-effective training sample augmentation. In online phase, we train an online RL agent to dynamically decide the sample generation processes, drawing from either the learning-based policy or the expert policy. The objective here is to strike an optimal balance between the exploration and exploitation of the sample generation process. In offline phase, a value function is trained to fit the cumulative reward for each decision and to filter the samples with high cumulative returns. This dual-purpose function not only reduces training complexity but also enhances the quality of the samples. To assess the efficacy of our proposed data augmentation mechanism, we conduct comprehensive evaluations across a range of MIP problems. The results consistently show that our method excels in making superior branching decisions compared to state-of-the-art learning-based models and the open-source solver SCIP. Notably, it even often outperforms the commercial solver Gurobi. | [] | [] | Towards Imitation Learning to Branch for MIP: A Hybrid Reinforcement Learning based Sample Augmentation Approach | [
"Changwen Zhang",
"Wenli Ouyang",
"Hao Yuan",
"Liming Gong",
"Yong Sun",
"Ziao Guo",
"Zhichen Dong",
"Junchi Yan"
] | 18,781 | https://openreview.net/forum?id=NdcQQ82mfy |
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[] | Poster | [] | The goal of social alignment for AI systems is to make sure these models can conduct themselves appropriately following social values. Unlike humans who establish a consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly recite the corpus in social isolation, which causes poor generalization in unfamiliar cases and the lack of robustness under adversarial attacks. In this work, we introduce a new training paradigm that enables LMs to learn from simulated social interactions. Compared with existing methods, our method is much more scalable and efficient, and shows superior performance in alignment benchmarks and human evaluation. | [] | [] | Training Socially Aligned Language Models on Simulated Social Interactions | [
"Ruibo Liu",
"Ruixin Yang",
"Chenyan Jia",
"Ge Zhang",
"Diyi Yang",
"Soroush Vosoughi"
] | 2305.16960 | 18,780 | https://openreview.net/forum?id=NddKiWtdUm |
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[] | Poster | [] | Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL).We present an effective yet simple framework to extend the use of replays across multiple experiments, minimally adapting the RL workflow for sizeable improvements in controller performance and research iteration times.At its core, Replay across Experiments (RaE) involves reusing experience from previous experiments to improve exploration and bootstrap learning while reducing required changes to a minimum in comparison to prior work. We empirically show benefits across a number of RL algorithms and challenging control domains spanning both locomotion and manipulation, including hard exploration tasks from egocentric vision. Through comprehensive ablations, we demonstrate robustness to the quality and amount of data available and various hyperparameter choices. Finally, we discuss how our approach can be applied more broadly across research life cycles and can increase resilience by reloading data across random seeds or hyperparameter variations. | [] | [] | Replay across Experiments: A Natural Extension of Off-Policy RL | [
"Dhruva Tirumala",
"Thomas Lampe",
"Jose Enrique Chen",
"Tuomas Haarnoja",
"Sandy Huang",
"Guy Lever",
"Ben Moran",
"Tim Hertweck",
"Leonard Hasenclever",
"Martin Riedmiller",
"Nicolas Heess",
"Markus Wulfmeier"
] | 2311.15951 | 18,779 | https://openreview.net/forum?id=Nf4Lm6fXN8 |
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[] | Spotlight Poster | [] | Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress ("knowledge tracing"; KT), and the prerequisite structure of the learning domain ("knowledge mapping"). While recent deep learning models achieve high KT accuracy, they do so at the expense of the interpretability of psychologically-inspired models. In this work, we present a solution to this trade-off. PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics, thus achieving interpretability by design. Moreover, by using scalable Bayesian inference, PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and interaction data. Evaluated on three datasets from online learning platforms, PSI-KT achieves superior multi-step **p**redictive accuracy and **s**calable inference in continual-learning settings, all while providing **i**nterpretable representations of learner-specific traits and the prerequisite structure of knowledge that causally supports learning. In sum, predictive, scalable and interpretable knowledge tracing with solid knowledge mapping lays a key foundation for effective personalized learning to make education accessible to a broad, global audience. | [] | [] | Predictive, scalable and interpretable knowledge tracing on structured domains | [
"Hanqi Zhou",
"Robert Bamler",
"Charley M Wu",
"Álvaro Tejero-Cantero"
] | 2403.13179 | 18,778 | https://openreview.net/forum?id=NgaLU2fP5D |
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[] | Spotlight Poster | [] | We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims. TTE models are used for estimating the probability distribution of the time until a specific event occurs, which is an important task in medical settings. TTE models provide many advantages over classification using fixed time horizons, including naturally handling censored observations, but are challenging to train with limited labeled data. MOTOR addresses this challenge by pretraining on up to 55M patient records (9B clinical events). We evaluate MOTOR's transfer learning performance on 19 tasks, across 3 patient databases (a private EHR system, MIMIC-IV, and Merative claims data). Task-specific models adapted from MOTOR improve time-dependent C statistics by 4.6\% over state-of-the-art, improve label efficiency by up to 95\% ,and are more robust to temporal distributional shifts. We further evaluate cross-site portability by adapting our MOTOR foundation model for six prediction tasks on the MIMIC-IV dataset, where it outperforms all baselines. MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL]. | [] | [] | MOTOR: A Time-to-Event Foundation Model For Structured Medical Records | [
"Ethan Steinberg",
"Jason Alan Fries",
"Yizhe Xu",
"Nigam Shah"
] | 2301.03150 | 18,777 | https://openreview.net/forum?id=NialiwI2V6 |
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[] | Poster | [] | It is becoming common practice for natural language processing to finetune pretrained language models for several downstream tasks at the same time. In practice, one might see several use cases based on the same model running simultaneously. Yet, this practice comes with considerable storage requirements, an issue that becomes particularly acute when scaling to large models or deploying numerous per-user or per-task adapted models. Although parameter-efficient finetuning methods such as LoRA exist, they do not fully mitigate this storage challenge. To this end, we introduce Efficient Low-Rank Adaptation with Random Matrices (ELoRA), which takes parameter efficiency to the extreme. By freezing a single pair of random low-rank matrices, shared across all layers, and using small layer-wise trainable scaling vectors, ELoRA achieves a 10x reduction in trainable parameters compared to LoRA without compromising performance levels. We demonstrate the effectiveness of the method on the GLUE benchmark and analyze its parameter-performance trade-off. Finally, using the Llama2 7B model, we show that ELoRA can also be used for instruction-tuning with merely 1.4M parameters. | [] | [] | VeRA: Vector-based Random Matrix Adaptation | [
"Dawid Jan Kopiczko",
"Tijmen Blankevoort",
"Yuki M Asano"
] | 2310.11454 | 18,775 | https://openreview.net/forum?id=NjNfLdxr3A |
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[] | Poster | [] | Numerous generalization bounds have been proposed in the literature as potential explanations for the ability of neural networks to generalize in the overparameterized setting. However, none of these bounds are tight. For instance, in their paper “Fantastic Generalization Measures and Where to Find Them”, Jiang et al. (2020) examine more than a dozen generalization bounds, and show empirically that none of them imply guarantees that can explain the remarkable performance of neural networks. This raises the question of whether tight generalization bounds are at all possible. We consider two types of generalization bounds common in the literature: (1) bounds that depend on the training set and the output of the learning algorithm. There are multiple bounds of this type in the literature (e.g., norm- and margin-based bounds), but we prove mathematically that no such bound can be uniformly tight in the overparameterized setting; (2) bounds that depend on the training set and on the learning algorithm (e.g., stability bounds). For these bounds, we show a trade-off between the algorithm's performance and the bound's tightness. Namely, under mild assumptions, if the algorithm achieves good accuracy in the overparameterized setting, then no generalization bound can be tight for it. We conclude that generalization bounds in the overparameterized setting cannot be tight without suitable assumptions on the population distribution. | [] | [] | Fantastic Generalization Measures are Nowhere to be Found | [
"Michael Gastpar",
"Ido Nachum",
"Jonathan Shafer",
"Thomas Weinberger"
] | 2309.13658 | 18,773 | https://openreview.net/forum?id=NkmJotfL42 |
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[] | Poster | [] | Recent studies have presented compelling evidence that large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world, which marks an initial step toward versatile robotics.However, these efforts tend to overlook the visual richness of open worlds, rendering the entire interactive process akin to ``a blindfolded text-based game.''Consequently, LLM-based agents frequently encounter challenges in intuitively comprehending their surroundings and producing responses that are easy to understand.In this paper, we propose Steve-Eye, an end-to-end trained large multimodal model designed to address this limitation.Steve-Eye integrates the LLM with a visual encoder which enables it to process visual-text inputs and generate multimodal feedback.In addition, we use a semi-automatic strategy to collect an extensive dataset comprising 850K open-world instruction pairs, empowering our model to encompass three essential functions for an agent: multimodal perception, foundational knowledge base, and skill prediction and planning.Lastly, we develop three open-world evaluation benchmarks, then carry out extensive experiments from a wide range of perspectives to validate our model's capability to strategically act and plan.Codes and datasets will be released. | [] | [] | Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds | [
"Sipeng Zheng",
"jiazheng liu",
"Yicheng Feng",
"Zongqing Lu"
] | 18,772 | https://openreview.net/forum?id=NltzxpG0nz |
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[] | Poster | [] | Human visual recognition system shows astonishing capability of compressing visual information into a set of tokens containing rich representations without label supervision. One critical driving principle behind it is perceptual grouping. Despite being widely used in computer vision in the early 2010s, it remains a mystery whether perceptual grouping can be leveraged to derive a neural visual recognition backbone that generates as powerful representations. In this paper, we propose the Perceptual Group Tokenizer, a model that entirely relies on grouping operations to extract visual features and perform self-supervised representation learning, where a series of grouping operations are used to iteratively hypothesize the context for pixels or superpixels to refine feature representations. We show that the proposed model can achieve competitive performance compared to state-of-the-art vision architectures, and inherits desirable properties including adaptive computation without re-training, and interpretability. Specifically, Perceptual Group Tokenizer achieves 79.7% on ImageNet-1K self-supervised learning benchmark with linear probe evaluation, marking a new progress under this paradigm. | [] | [] | Perceptual Group Tokenizer: Building Perception with Iterative Grouping | [
"Zhiwei Deng",
"Ting Chen",
"Yang Li"
] | 2311.18296 | 18,771 | https://openreview.net/forum?id=NnYaYVODyV |
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[] | Poster | [] | In the real world, the strong episode resetting mechanisms that are needed to trainagents in simulation are unavailable. The resetting assumption limits the potentialof reinforcement learning in the real world, as providing resets to an agent usuallyrequires the creation of additional handcrafted mechanisms or human interventions.Recent work aims to train agents (forward) with learned resets by constructinga second (backward) agent that returns the forward agent to the initial state. Wefind that the termination and timing of the transitions between these two agentsare crucial for algorithm success. With this in mind, we create a new algorithm,Reset Free RL with Intelligently Switching Controller (RISC) which intelligentlyswitches between the two agents based on the agent’s confidence in achieving itscurrent goal. Our new method achieves state-of-the-art performance on severalchallenging environments for reset-free RL. | [] | [] | Intelligent Switching for Reset-Free RL | [
"Darshan Patil",
"Janarthanan Rajendran",
"Glen Berseth",
"Sarath Chandar"
] | 18,769 | https://openreview.net/forum?id=Nq45xeghcL |
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[] | Poster | [] | Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice variability) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech language understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompts based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality text prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available (https://speechresearch.github.io/prompttts2). | [] | [] | PromptTTS 2: Describing and Generating Voices with Text Prompt | [
"Yichong Leng",
"ZHifang Guo",
"Kai Shen",
"Zeqian Ju",
"Xu Tan",
"Eric Liu",
"Yufei Liu",
"Dongchao Yang",
"leying zhang",
"Kaitao Song",
"Lei He",
"Xiangyang Li",
"sheng zhao",
"Tao Qin",
"Jiang Bian"
] | 2309.02285 | 18,768 | https://openreview.net/forum?id=NsCXDyv2Bn |
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[] | Spotlight Poster | [] | We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time.By construction, the proposed LFlows satisfy the continuity equation,a PDE describing mass conservation in its differentiable form. Our model is based on the insight that solutions to the continuity equation can be expressed astime-dependent density transformations via differentiable and invertible maps.This follows from classical theory of the existence and uniqueness of Lagrangian flows for smooth vector fields.Hence, we model fluid densities by transforming a base density with parameterized diffeomorphisms conditioned on time.The key benefit compared to methods relying on numerical ODE solvers or PINNs is that the analytic expression of the velocity is always consistent with changes in density.Furthermore, we require neither expensive numerical solvers, nor additional penalties to enforce the PDE.LFlows show higher predictive accuracy in density modeling tasks compared to competing models in 2D and 3D,while being computationally efficient.As a real-world application, we model bird migration based on sparse weather radar measurements. | [] | [] | Lagrangian Flow Networks for Conservation Laws | [
"Fabricio Arend Torres",
"Marcello Massimo Negri",
"Marco Inversi",
"Jonathan Aellen",
"Volker Roth"
] | 2305.16846 | 18,767 | https://openreview.net/forum?id=Nshk5YpdWE |
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