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Spotlight Poster
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Control of dynamic systems involving hybrid actions is a challenging task in robotics. To address this, we present a novel algorithm called Generalized Policy Iteration using Tensor Train (TTPI) that belongs to the class of Approximate Dynamic Programming (ADP). We use a low-rank tensor approximation technique called Tensor Train (TT) to approximate the state-value and advantage function which enables us to efficiently handle hybrid systems. We demonstrate the superiority of our approach over previous baselines for some benchmark problems with hybrid action spaces. Additionally, the robustness and generalization of the policy for hybrid systems are showcased through a real-world robotics experiment involving a non-prehensile manipulation task which is considered to be a highly challenging control problem.
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Generalized Policy Iteration using Tensor Approximation for Hybrid Control
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[
"Suhan Shetty",
"Teng Xue",
"Sylvain Calinon"
] | 18,281 |
https://openreview.net/forum?id=csukJcpYDe
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Poster
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We propose a new model class aimed at predicting dynamical trajectories from high-dimensional empirical data. This is done by combining variational autoencoders and spatio-temporal attention within a framework designed to enforce certain scientifically-motivated invariances.The models allow inference of systembehaviour at any continuous time and generalization well beyond the data distributions seen during training.Furthermore, the models do not require anexplicit neural ODE formulation, making them efficient and highly scalable in practice.We study behaviour through simple theoretical analyses and extensive experiments on synthetic and real-world datasets. The latter investigate the ability to predict the trajectories of very complicated systems based on finite data and show that the proposed approaches can outperform existing neural-dynamical models.We study also more general inductive bias in the context of transfer to data obtained under entirely novel system interventions. Overall, our results provide a new framework for efficiently learning complicated dynamics in a data-driven manner, with potential applications in a wide range of fields including physics, biology, and engineering.
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Invariance-based Learning of Latent Dynamics
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[
"Kai Lagemann",
"Christian Lagemann",
"Sach Mukherjee"
] | 19,103 |
https://openreview.net/forum?id=EWTFMkTdkT
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Spotlight Poster
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[] |
State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of representation learning for CATE estimation is typically violated. In this paper, we propose a new, representation-agnostic framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation. First, we establish theoretically under which conditions CATEs are non-identifiable given low-dimensional (constrained) representations. Second, as our remedy, we propose to perform partial identification of CATEs or, equivalently, aim at estimating of upper and lower bounds of the representation-induced confounding bias. We demonstrate the effectiveness of our bounds in a series of experiments. In sum, our framework is of direct relevance in practice where the validity of CATE estimation is of importance.
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Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
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[
"Valentyn Melnychuk",
"Dennis Frauen",
"Stefan Feuerriegel"
] |
2311.11321
| 18,278 |
https://openreview.net/forum?id=d3xKPQVjSc
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Poster
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[] |
In the pursuit of reducing the number of trainable parameters in deep transformer networks, we employ Reinforcement Learning to dynamically select layers during training and tie them together. Every few iterations, the RL agent is asked whether to train each layer $i$ independently or to copy the weights of a previous layer $j<i$. This facilitates weight sharing, reduces the number of trainable parameters, and also serves as an effective regularization technique. Experimental evaluations validate that our model modestly outperforms the baseline transformer model with regard to perplexity and drastically reduces the number of trainable parameters. In particular, the memory consumption during training is up to one order of magnitude less than the conventional training method.
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Dynamic Layer Tying for Parameter-Efficient Transformers
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[
"Tamir David Hay",
"Lior Wolf"
] |
2401.12819
| 18,276 |
https://openreview.net/forum?id=d4uL2MSe0z
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[] |
Poster
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[] |
In recent research, Tensor Product Representation (TPR) is applied for the systematic generalization task of deep neural networks by learning the compositional structure of data. However, such prior works show limited performance in discovering and representing the symbolic structure from unseen test data because of the incomplete bindings to the structural representations. In this work, we propose an Attention-based Iterative Decomposition (AID) module that can effectively improve the binding for the structured representations encoded from the sequential input features with TPR. Our AID can be easily adapted to any TPR-based model and provides enhanced systematic decomposition through a competitive attention mechanism between input features and structured representations. In our experiments, AID shows effectiveness by significantly improving the performance of TPR-based prior works on the series of systematic generalization tasks. Moreover, in the quantitative and qualitative evaluations, AID produces more compositional and well-bound structural representations than other works.
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Attention-based Iterative Decomposition for Tensor Product Representation
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[
"Taewon Park",
"Inchul Choi",
"Minho Lee"
] | 19,077 |
https://openreview.net/forum?id=FDb2JQZsFH
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Poster
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[] |
Generalization to out-of-distribution (OOD) data is a critical challenge in machine learning. Ensemble-based methods, like weight space ensembles that interpolate model parameters, have been shown to achieve superior OOD performance. However, the underlying mechanism for their effectiveness remains unclear. In this study, we closely examine WiSE-FT, a popular weight space ensemble method that interpolates between a pre-trained and a fine-tuned model. We observe an unexpected ``FalseFalseTrue" phenomenon, in which WiSE-FT successfully corrects many cases where each individual model makes incorrect predictions, which contributes significantly to its OOD effectiveness. To gain further insights, we conduct theoretical analysis in a multi-class setting with a large number of spurious features. Our analysis predicts the above phenomenon and it further shows that ensemble-based models reduce prediction errors in the OOD settings by utilizing a more diverse set of spurious features. Contrary to the conventional wisdom that focuses on learning invariant features for better OOD performance, our findings suggest that incorporating a large number of diverse spurious features weakens their individual contributions, leading to improved overall OOD generalization performance. Empirically we demonstrate the effectiveness of utilizing diverse spurious features on a MultiColorMNIST dataset, and our experimental results are consistent with the theoretical analysis. Building upon the new theoretical insights into the efficacy of ensemble methods, we further identify an issue of WiSE-FT caused by the overconfidence of fine-tuned models in OOD situations. This overconfidence magnifies the fine-tuned model's incorrect prediction, leading to deteriorated OOD ensemble performance. To remedy this problem, we propose a novel method called BAlaNced averaGing (BANG) to mitigate the overconfidence problem, which significantly enhances the OOD performance of WiSE-FT.
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Spurious Feature Diversification Improves Out-of-distribution Generalization
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[
"LIN Yong",
"Lu Tan",
"Yifan HAO",
"Ho Nam Wong",
"Hanze Dong",
"WEIZHONG ZHANG",
"Yujiu Yang",
"Tong Zhang"
] |
2309.17230
| 18,275 |
https://openreview.net/forum?id=d6H4RBi7RH
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Poster
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[] |
Parameter-efficient fine-tuning (PEFT) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs), with low-rank adaptation (LoRA) being a widely adopted choice. However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets. Bayesian methods, with their inherent ability to estimate uncertainty, serve as potent tools to mitigate overconfidence and enhance calibration. In this work, we introduce Laplace-LoRA, a straightforward yet effective Bayesian method, which applies the Laplace approximation to the LoRA parameters and, considerably boosts the calibration of fine-tuned LLMs.
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Bayesian Low-rank Adaptation for Large Language Models
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[
"Adam X. Yang",
"Maxime Robeyns",
"Xi Wang",
"Laurence Aitchison"
] |
2308.13111
| 19,071 |
https://openreview.net/forum?id=FJiUyzOF1m
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Poster
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[] |
The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality. This paper proposes one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding our study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on the posterior, and explore methods for visualizing the posterior. Moreover, we uncover weight-space symmetries as a critical aspect for understanding the posterior. To this extent, we develop an in-depth assessment of the impact of both permutation and scaling symmetries that tend to obfuscate the Bayesian posterior. While the first type of transformation is known for duplicating modes, we explore the relationship between the latter and L2 regularization, challenging previous misconceptions. Finally, to help the community improve our understanding of the Bayesian posterior, we will release the first large-scale checkpoint dataset, including thousands of real-world models, along with our codes, after the anonymity period.
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A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
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[
"Olivier Laurent",
"Emanuel Aldea",
"Gianni Franchi"
] |
2310.08287
| 19,066 |
https://openreview.net/forum?id=FOSBQuXgAq
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[] |
Poster
|
[
"https://github.com/chanchimin/ChatEval"
] |
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results suggest that further advancements are needed to bridge the gap between their current effectiveness and human-level evaluation quality.Recognizing that best practices of human evaluation processes often involve multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies.In this paper, we construct a multi-agent referee team called $\textbf{ChatEval}$ to autonomously discuss and evaluate the quality of different texts. Our experiments on two benchmarks illustrate that ChatEval delivers superior accuracy and correlation in alignment with human assessment. Furthermore, we find that the diverse role prompts (different personas) are essential in the multi-agent debate process; that is, utilizing the same role description in the prompts can lead to a degradation in performance. Our qualitative analysis also shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
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ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
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[
"Chi-Min Chan",
"Weize Chen",
"Yusheng Su",
"Jianxuan Yu",
"Wei Xue",
"Shanghang Zhang",
"Jie Fu",
"Zhiyuan Liu"
] |
2308.07201
| 19,065 |
https://openreview.net/forum?id=FQepisCUWu
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[] |
Spotlight Poster
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[] |
Non-transferable learning (NTL) aims to restrict the generalizability of models toward the target domain(s). To this end, existing works learn non-transferable representations by reducing statistical dependence between the source and target domain. However, such statistical methods essentially neglect to distinguish between *styles* and *contents*, leading them to inadvertently fit (i) spurious correlation between *styles* and *labels*, and (ii) fake independence between *contents* and *labels*. Consequently, their performance will be limited when natural distribution shifts occur or malicious intervention is imposed. In this paper, we propose a novel method (dubbed as H-NTL) to understand and advance the NTL problem by introducing a causal model to separately model *content* and *style* as two latent factors, based on which we disentangle and harness them as guidances for learning non-transferable representations with intrinsically causal relationships. Specifically, to avoid fitting spurious correlation and fake independence, we propose a variational inference framework to disentangle the naturally mixed *content factors* and *style factors* under our causal model. Subsequently, based on dual-path knowledge distillation, we harness the disentangled two *factors* as guidances for non-transferable representation learning: (i) we constraint the source domain representations to fit *content factors* (which are the intrinsic cause of *labels*), and (ii) we enforce that the target domain representations fit *style factors* which barely can predict labels. As a result, the learned feature representations follow optimal untransferability toward the target domain and minimal negative influence on the source domain, thus enabling better NTL performance. Empirically, the proposed H-NTL significantly outperforms competing methods by a large margin.
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Improving Non-Transferable Representation Learning by Harnessing Content and Style
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[
"Ziming Hong",
"Zhenyi Wang",
"Li Shen",
"Yu Yao",
"Zhuo Huang",
"Shiming Chen",
"Chuanwu Yang",
"Mingming Gong",
"Tongliang Liu"
] | 19,060 |
https://openreview.net/forum?id=FYKVPOHCpE
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Poster
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[] |
Seminal research in the field of graph neural networks (GNNs) has revealed a direct correspondence between the expressive capabilities of GNNs and the $k$-dimensional Weisfeiler-Leman ($k$WL) test, a widely-recognized method for verifying graph isomorphism. This connection has reignited interest in comprehending the specific graph properties effectively distinguishable by the $k$WL test.A central focus of research in this field revolves around determining the least dimensionality $k$, for which $k$WL can discern graphs with different number of occurrences of a pattern graph $p$. We refer to such a least $k$ as the WL-dimension of this pattern counting problem. This inquiry traditionally delves into two distinct counting problems related to patterns: subgraph counting and induced subgraph counting. Intriguingly, despite their initial appearance as separate challenges with seemingly divergent approaches, both of these problems are interconnected components of a more comprehensive problem: "graph motif parameters". In this paper, we provide a precise characterization of the WL-dimension of labeled graph motif parameters. As specific instances of this result, we obtain characterizations of the WL-dimension of the subgraph counting and induced subgraph counting problem for every labeled pattern $p$. Particularly noteworthy is our resolution of a problem left open in previous work concerning induced copies.We additionally demonstrate that in cases where the $k$WL test distinguishes between graphs with varying occurrences of a pattern $p$, the exact number of occurrences of $p$ can be computed uniformly using only local information of the last layer of a corresponding GNN.We finally delve into the challenge of recognizing the WL-dimension of various graph parameters. We give a polynomial time algorithm for determining the WL-dimension of the subgraph counting problem for given pattern $p$, answering an open question from previous work.We additionally show how to utilize deep results from the field of graph motif parameters, together with our characterization, to determine the WL-dimension of induced subgraph counting and counting $k$-graphlets.
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[] |
On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
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[
"Matthias Lanzinger",
"Pablo Barcelo"
] |
2309.17053
| 19,057 |
https://openreview.net/forum?id=FddFxi08J3
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[] |
Poster
|
[
"https://github.com/jy0205/LaVIT"
] |
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks.
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[] |
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
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[
"Yang Jin",
"Kun Xu",
"Kun Xu",
"Liwei Chen",
"Chao Liao",
"Jianchao Tan",
"Quzhe Huang",
"Bin CHEN",
"Chengru Song",
"dai meng",
"Di ZHANG",
"Wenwu Ou",
"Kun Gai",
"Yadong MU"
] |
2309.04669
| 19,049 |
https://openreview.net/forum?id=FlvtjAB0gl
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|
[
"guoyww/animatediff"
] |
Spotlight Poster
|
[
"https://github.com/guoyww/AnimateDiff"
] |
With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity.
|
[
"guoyww/AnimateDiff"
] |
[] |
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
|
[
"Yuwei Guo",
"Ceyuan Yang",
"Anyi Rao",
"Zhengyang Liang",
"Yaohui Wang",
"Yu Qiao",
"Maneesh Agrawala",
"Dahua Lin",
"Bo Dai"
] |
2307.04725
| 19,044 |
https://openreview.net/forum?id=Fx2SbBgcte
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[] |
Poster
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[] |
Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surprisingly occur in linear networks performing linear tasks in a simple teacher-student setup. In this setting, the full training dynamics is derived in terms of the expected training and generalization data covariance matrix. We present exact predictions on how the grokking time depends on input and output dimensionality, train sample size, regularization, and network parameters initialization. The key findings are that late generalization increase may not imply a transition from "memorization" to "understanding", but can simply be an artifact of the accuracy measure. We provide empirical verification for these propositions, along with preliminary results indicating that some predictions also hold for deeper networks, with non-linear activations.
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[] |
Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
|
[
"Noam Itzhak Levi",
"Alon Beck",
"Yohai Bar-Sinai"
] |
2310.16441
| 19,036 |
https://openreview.net/forum?id=GH2LYb9XV0
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[] |
Poster
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[
"https://github.com/PetrMokrov/Energy-guided-Entropic-OT"
] |
Energy-based models (EBMs) are known in the Machine Learning community for decades. Since the seminal works devoted to EBMs dating back to the noughties, there have been a lot of efficient methods which solve the generative modelling problem by means of energy potentials (unnormalized likelihood functions). In contrast, the realm of Optimal Transport (OT) and, in particular, neural OT solvers is much less explored and limited by few recent works (excluding WGAN-based approaches which utilize OT as a loss function and do not model OT maps themselves). In our work, we bridge the gap between EBMs and Entropy-regularized OT. We present a novel methodology which allows utilizing the recent developments and technical improvements of the former in order to enrich the latter. From the theoretical perspective, we prove generalization bounds for our technique. In practice, we validate its applicability in toy 2D and image domains. To showcase the scalability, we empower our method with a pre-trained StyleGAN and apply it to high-res AFHQ $512\times512$ unpaired I2I translation. For simplicity, we choose simple short- and long-run EBMs as a backbone of our Energy-guided Entropic OT approach, leaving the application of more sophisticated EBMs for future research. Our code is available at: https://github.com/PetrMokrov/Energy-guided-Entropic-OT
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[] |
Energy-guided Entropic Neural Optimal Transport
|
[
"Petr Mokrov",
"Alexander Korotin",
"Alexander Kolesov",
"Nikita Gushchin",
"Evgeny Burnaev"
] |
2304.06094
| 18,274 |
https://openreview.net/forum?id=d6tUsZeVs7
|
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[] |
Spotlight Poster
|
[
"https://github.com/ernie-research/Tool-Augmented-Reward-Model"
] |
Reward modeling (*a.k.a.*, preference modeling) is instrumental for aligning large language models with human preferences, particularly within the context of reinforcement learning from human feedback (RLHF). While conventional reward models (RMs) have exhibited remarkable scalability, they oft struggle with fundamental functionality such as arithmetic computation, code execution, and factual lookup. In this paper, we propose a tool-augmented preference modeling approach, named Themis, to address these limitations by empowering RMs with access to external environments, including calculators and search engines. This approach not only fosters synergy between tool utilization and reward grading but also enhances interpretive capacity and scoring reliability. Our study delves into the integration of external tools into RMs, enabling them to interact with diverse external sources and construct task-specific tool engagement and reasoning traces in an autoregressive manner. We validate our approach across a wide range of domains, incorporating seven distinct external tools. Our experimental results demonstrate a noteworthy overall improvement of 17.7% across eight tasks in preference ranking. Furthermore, our approach outperforms Gopher 280B by 7.3% on TruthfulQA task in zero-shot evaluation. In human evaluations, RLHF trained with Themis attains an average win rate of 32% when compared to baselines across four distinct tasks. Additionally, we provide a comprehensive collection of tool-related RM datasets, incorporating data from seven distinct tool APIs, totaling 15,000 instances. We have made the code, data, and model checkpoints publicly available to facilitate and inspire further research advancements (https://github.com/ernie-research/Tool-Augmented-Reward-Model).
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[] |
Tool-Augmented Reward Modeling
|
[
"Lei Li",
"Yekun Chai",
"Shuohuan Wang",
"Yu Sun",
"Hao Tian",
"Ningyu Zhang",
"Hua Wu"
] |
2310.01045
| 18,272 |
https://openreview.net/forum?id=d94x0gWTUX
|
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[] |
Poster
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[] |
Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent's behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e., solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters.
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[] |
[] |
Domain Randomization via Entropy Maximization
|
[
"Gabriele Tiboni",
"Pascal Klink",
"Jan Peters",
"Tatiana Tommasi",
"Carlo D'Eramo",
"Georgia Chalvatzaki"
] |
2311.01885
| 19,025 |
https://openreview.net/forum?id=GXtmuiVrOM
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[] |
Poster
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[] |
We study the problem of classification with a reject option for a fixed predictor, crucial to natural language processing. We introduce a new problem formulation for this scenario, and an algorithm minimizing a new surrogate loss function. We provide a complete theoretical analysis of the surrogate loss function with a strong $H$-consistency guarantee. For evaluation, we choose the \textit{decontextualization} task, and provide a manually-labelled dataset of $2\mathord,000$ examples. Our algorithm significantly outperforms the baselines considered, with a $\sim 25$% improvement in coverage when halving the error rate, which is only $\sim 3$% away from the theoretical limit.
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[] |
[] |
Learning to Reject with a Fixed Predictor: Application to Decontextualization
|
[
"Christopher Mohri",
"Daniel Andor",
"Eunsol Choi",
"Michael Collins",
"Anqi Mao",
"Yutao Zhong"
] |
2301.09044
| 18,271 |
https://openreview.net/forum?id=dCHbFDsCZz
|
|
[] |
Poster
|
[] |
Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk-sensitive MDPs, which assesses risk in a slightly different manner compared to the classical Markov risk measure \cite{ruszczynski2010risk}, and establishes its equivalence with a class of regularized robust MDP (RMDP) problems, including the standard RMDP as a special case. Leveraging this equivalence, we further derive the policy gradient theorem for both problems, proving gradient domination and global convergence of the exact policy gradient method under the tabular setting with direct parameterization. This forms a sharp contrast to the Markov risk measure, known to be potentially non-gradient-dominant \cite{huang2021convergence}. We also propose a sample-based offline learning algorithm, namely the robust fitted-Z iteration (RFZI), for a specific regularized RMDP problem with a KL-divergence regularization term (or equivalently the risk-sensitive MDP with an entropy risk measure). We showcase its streamlined design and less stringent assumptions due to the equivalence and analyze its sample complexity.
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[] |
[] |
Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
|
[
"Runyu Zhang",
"Yang Hu",
"Na Li"
] |
2306.11626
| 18,270 |
https://openreview.net/forum?id=dEz3ge8QSo
|
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[] |
Spotlight Poster
|
[] |
Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excellent tool-use capabilities of state-of-the-art (SOTA) closed-source LLMs, e.g., ChatGPT. To bridge this gap, we introduce ToolLLM, a general tool-use framework encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is constructed automatically using ChatGPT. Specifically, the construction can be divided into three stages: (i) API collection: we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub; (ii) instruction generation: we prompt ChatGPT to generate diverse instructions involving these APIs, covering both single-tool and multi-tool scenarios; (iii) solution path annotation: we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To enhance the reasoning capabilities of LLMs, we develop a novel depth-first search-based decision tree algorithm. It enables LLMs to evaluate multiple reasoning traces and expand the search space. Moreover, to evaluate the tool-use capabilities of LLMs, we develop an automatic evaluator: ToolEval. Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction. Experiments show that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. Our ToolLLaMA also demonstrates strong zero-shot generalization ability in an out-of-distribution tool-use dataset: APIBench.
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[] |
[] |
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
|
[
"Yujia Qin",
"Shihao Liang",
"Yining Ye",
"Kunlun Zhu",
"Lan Yan",
"Yaxi Lu",
"Yankai Lin",
"Xin Cong",
"Xiangru Tang",
"Bill Qian",
"Sihan Zhao",
"Lauren Hong",
"Runchu Tian",
"Ruobing Xie",
"Jie Zhou",
"Mark Gerstein",
"dahai li",
"Zhiyuan Liu",
"Maosong Sun"
] |
2307.16789
| 18,267 |
https://openreview.net/forum?id=dHng2O0Jjr
|
|
[] |
Poster
|
[
"https://github.com/Hritikbansal/sparse_feedback"
] |
Aligning large language models (LLMs) with human values and intents critically involves the use of human or AI feedback. While dense feedback annotations are expensive to acquire and integrate, sparse feedback presents a structural design choice between ratings (e.g., score Response A on a scale of 1-7) and rankings (e.g., is Response A better than Response B?). In this work, we analyze the effect of this design choice for the alignment and evaluation of LLMs. We uncover an inconsistency problem wherein the preferences inferred from ratings and rankings significantly disagree 60% for both human and AI annotators. Our subsequent analysis identifies various facets of annotator biases that explain this phenomena such as human annotators would rate denser responses higher while preferring accuracy during pairwise judgments, for a particular comparison instance. To our surprise, we observe that the choice of feedback protocol has a significant effect on the evaluation of aligned LLMs. In particular, we find that LLMs that leverage rankings data for alignment (say model X) are preferred over those that leverage ratings data (say model Y), with a rank-based evaluation protocol (is X/Y's response better than reference response?) but not with a rating-based evaluation protocol (score Rank X/Y's response on a scale of 1-7). Our findings thus shed light on critical gaps in methods for evaluating the real-world utility of language models and their strong dependence on the feedback protocol used for alignment.
|
[] |
[] |
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models
|
[
"Hritik Bansal",
"John Dang",
"Aditya Grover"
] |
2308.15812
| 18,266 |
https://openreview.net/forum?id=dKl6lMwbCy
|
|
[] |
Poster
|
[] |
Multimodal pre-trained models have shown impressive potential in enhancing performance on downstream tasks. However, existing fusion strategies for modalities primarily rely on explicit interaction structures that fail to capture the diverse aspects and patterns inherent in input data. This yields limited performance in zero-shot contexts, especially when fine-grained classifications and abstract interpretations are required. To address this, we propose an effective approach, namely Prompt Learning with Quaternion Networks (QNet), for semantic alignment across diverse modalities. QNet employs a quaternion hidden space where the mutually orthogonal imaginary axes capture rich intermodal semantic spatial correlations from various perspectives. Hierarchical features across multilayers are utilized to encode intricate interdependencies within various modalities with reduced parameters. Our experiments on 11 datasets demonstrate that QNet outperforms state-of-the-art prompt learning techniques in base-to-novel generalization, cross-dataset transfer, and domain transfer scenarios with fewer learnable parameters. Our code and models will be publicly available.
|
[] |
[] |
Prompt Learning with Quaternion Networks
|
[
"Boya Shi",
"Zhengqin Xu",
"Shuai Jia",
"Chao Ma"
] | 18,265 |
https://openreview.net/forum?id=dKlxDx2SoS
|
||
[] |
Poster
|
[] |
This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries. For every problem considered in the non-monotone setting, the proposed algorithms are either the first with proven sub-linear $\alpha$-regret bounds or have better $\alpha$-regret bounds than the state of the art, where $\alpha$ is a corresponding approximation bound in the offline setting. In the monotone setting, the proposed approach gives state-of-the-art sub-linear $\alpha$-regret bounds among projection-free algorithms in 7 of the 8 considered cases while matching the result of the remaining case. Additionally, this paper addresses semi-bandit and bandit feedback for adversarial DR-submodular optimization, advancing the understanding of this optimization area.
|
[] |
[] |
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
|
[
"Mohammad Pedramfar",
"Yididiya Y. Nadew",
"Christopher John Quinn",
"Vaneet Aggarwal"
] |
2403.10063
| 19,007 |
https://openreview.net/forum?id=H4A9e8HvIn
|
|
[] |
Poster
|
[] |
In light of recent advancements in generative AI models, it has become essential to distinguish genuine content from AI-generated one to prevent the malicious usage of fake materials as authentic ones and vice versa. Various techniques have been introduced for identifying AI-generated images, with watermarking emerging as a promising approach. In this paper, we analyze the robustness of various AI-image detectors including watermarking and classifier-based deepfake detectors. For watermarking methods that introduce subtle image perturbations (i.e., low perturbation budget methods), we reveal a fundamental trade-off between the evasion error rate (i.e., the fraction of watermarked images detected as non-watermarked ones) and the spoofing error rate (i.e., the fraction of non-watermarked images detected as watermarked ones) upon an application of a diffusion purification attack. In this regime, we also empirically show that diffusion purification effectively removes watermarks with minimal changes to images. For high perturbation watermarking methods where notable changes are applied to images, the diffusion purification attack is not effective. In this case, we develop a model substitution adversarial attack that can successfully remove watermarks. Moreover, we show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images (potentially obscene) identified as watermarked ones, damaging the reputation of the developers. In particular, by just having black-box access to the watermarking method, we show that one can generate a watermarked noise image which can be added to the real images to have them falsely flagged as watermarked ones. Finally, we extend our theory to characterize a fundamental trade-off between the robustness and reliability of classifier-based deep fake detectors and demonstrate it through experiments.
|
[] |
[] |
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
|
[
"Mehrdad Saberi",
"Vinu Sankar Sadasivan",
"Keivan Rezaei",
"Aounon Kumar",
"Atoosa Chegini",
"Wenxiao Wang",
"Soheil Feizi"
] |
2310.00076
| 18,264 |
https://openreview.net/forum?id=dLoAdIKENc
|
|
[] |
Poster
|
[] |
The complexity of many tasks and environments can often be decomposed into simpler, independent modules.Discovering underlying compositional structure has the potential to expedite adaptation and enable _compositional generalization_.Despite progress, our most powerful systems struggle to compose flexibly.While most of these systems are monolithic, modularity promises to allow capturing the compositional nature of many tasks.However, it is unclear under which circumstances modular systems discover this hidden compositional structure.To shed light on this question, we study a teacher-student setting with a modular teacher where we have full control over the composition of ground truth modules.This allows us to relate the problem of compositional generalization to that of identification of the underlying modules.We show theoretically that identification up to linear transformation purely from demonstrations is possible in hypernetworks without having to learn an exponential number of module combinations.While our theory assumes the infinite data limit, in an extensive empirical study we demonstrate how meta-learning from finite data can discover modular solutions that generalize compositionally in modular but not monolithic architectures.We further show that our insights translate outside the teacher-student setting and demonstrate how modularity implemented by hypernetworks allows discovering compositional behavior policies and action-value functions.
|
[] |
[] |
Discovering modular solutions that generalize compositionally
|
[
"Simon Schug",
"Seijin Kobayashi",
"Yassir Akram",
"Maciej Wolczyk",
"Alexandra Maria Proca",
"Johannes Von Oswald",
"Razvan Pascanu",
"Joao Sacramento",
"Angelika Steger"
] |
2312.15001
| 19,005 |
https://openreview.net/forum?id=H98CVcX1eh
|
|
[] |
Poster
|
[] |
As a sub-discipline of evolutionary and computational linguistics, emergent communication (EC) studies communication protocols, called emergent languages, arising in simulations where agents communicate. A key goal of EC is to give rise to languages that share statistical properties with natural languages. In this paper, we reinterpret Lewis's signaling game, a frequently used setting in EC, as beta-VAE and reformulate its objective function as ELBO. Consequently, we clarify the existence of prior distributions of emergent languages and show that the choice of the priors can influence their statistical properties. Specifically, we address the properties of word lengths and segmentation, known as Zipf's law of abbreviation (ZLA) and Harris's articulation scheme (HAS), respectively. It has been reported that the emergent languages do not follow them when using the conventional objective. We experimentally demonstrate that by selecting an appropriate prior distribution, more natural segments emerge, while suggesting that the conventional one prevents the languages from following ZLA and HAS.
|
[] |
[] |
Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments
|
[
"Ryo Ueda",
"Tadahiro Taniguchi"
] |
2311.04453
| 19,004 |
https://openreview.net/forum?id=HC0msxE3sf
|
|
[] |
Poster
|
[] |
Dual-encoder models have demonstrated significant success in dense retrieval tasks for open-domain question answering that mostly involves zero-shot and few-shot scenarios. However, their performance in many-shot retrieval problems where training data is abundant, such as extreme multi-label classification (XMC), remains under-explored. Existing empirical evidence suggests that, for such problems, the dual-encoder method's accuracies lag behind the performance of state-of-the-art (SOTA) extreme classification methods that grow the number of learnable parameters linearly with the number of classes. As a result, some recent extreme classification techniques use a combination of dual-encoders and a learnable classification head for each class to excel on these tasks. In this paper, we investigate the potential of "pure" DE models in XMC tasks. Our findings reveal that when trained correctly standard dual-encoders can match or outperform SOTA extreme classification methods by up to 2% at Precision@1 even on the largest XMC datasets while being 20x smaller in terms of the number of trainable parameters. We further propose a differentiable topk error-based loss function, which can be used to specifically optimize for Recall@k metrics. We include our PyTorch implementation along with other resources for reproducing the results in the supplementary material.
|
[] |
[] |
Dual-Encoders for Extreme Multi-label Classification
|
[
"Nilesh Gupta",
"Fnu Devvrit",
"Ankit Singh Rawat",
"Srinadh Bhojanapalli",
"Prateek Jain",
"Inderjit S Dhillon"
] |
2310.10636
| 18,261 |
https://openreview.net/forum?id=dNe1T0Ahby
|
|
[] |
Poster
|
[] |
Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation methods that discard tokens below some probability threshold (the most common type of truncation) can guarantee that all sampled tokens have nonzero true probability. However, thresholds are a coarse heuristic, and necessarily discard some tokens with nonzero true probability as well. In pursuit of a more precise sampling strategy, we show that we can leverage a known source of model errors, the softmax bottleneck, to prove that certain tokens have nonzero true probability, without relying on a threshold. Based on our findings, we develop an experimental truncation strategy and the present pilot studies demonstrating the promise of this type of algorithm. Our evaluations show that our method outperforms its threshold-based counterparts under automatic and human evaluation metrics for low-entropy (i.e., close to greedy) open-ended text generation. Our theoretical findings and pilot experiments provide both insight into why truncation sampling works, and make progress toward more expressive sampling algorithms that better surface the generative capabilities of large language models.
|
[] |
[] |
Closing the Curious Case of Neural Text Degeneration
|
[
"Matthew Finlayson",
"John Hewitt",
"Alexander Koller",
"Swabha Swayamdipta",
"Ashish Sabharwal"
] |
2310.01693
| 18,260 |
https://openreview.net/forum?id=dONpC9GL1o
|
|
[] |
Poster
|
[] |
Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message passing framework. We formalize the notion of weight sharing in convolutional networks as the sharing of message functions over point-pairs that should be treated equally. We define equivalence classes of point-pairs that are identical up to a transformation in the group and derive attributes that uniquely identify these classes. Weight sharing is then obtained by conditioning message functions on these attributes. As an application of the theory, we develop an efficient equivariant group convolutional network for processing 3D point clouds. The theory of homogeneous spaces tells us how to do group convolutions with feature maps over the homogeneous space of positions $\mathbb{R}^3$, position and orientations $\mathbb{R}^3 {\times} S^2$, and the group $\mathrm{SE}(3)$ itself. Among these, $\mathbb{R}^3 {\times} S^2$ is an optimal choice due to the ability to represent directional information, which $\mathbb{R}^3$ methods cannot, and it significantly enhances computational efficiency compared to indexing features on the full $\mathrm{SE}(3)$ group. We empirically support this claim by reaching state-of-the-art results --in accuracy and speed-- on three different benchmarks: interatomic potential energy prediction, trajectory forecasting in N-body systems, and generating molecules via equivariant diffusion models.
|
[] |
[] |
Fast, Expressive $\mathrm{SE}(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
|
[
"Erik J Bekkers",
"Sharvaree Vadgama",
"Rob Hesselink",
"Putri A Van der Linden",
"David W. Romero"
] |
2310.02970
| 18,259 |
https://openreview.net/forum?id=dPHLbUqGbr
|
|
[] |
Poster
|
[] |
Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower bound, proving that under mild conditions, representation distortion inevitably occurs over time. To estimate the temporal distortion without human annotation after deployment, one naive approach is to pre-train a recurrent model (e.g., RNN) before deployment and use this model afterwards, but the estimation is far from satisfactory. In this paper, we analyze the representation distortion from an information theory perspective, and attribute it primarily to inaccurate feature extraction during evolution. Consequently, we introduce Smart, a straightforward and effective baseline enhanced by an adaptive feature extractor through self-supervised graph reconstruction. In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that Smart achieves good estimation performance. Moreover, we observe that Smart consistently shows outstanding generalization estimation on four real-world evolving graphs. The ablation studies underscore the necessity of graph reconstruction. For example, on OGB-arXiv dataset, the estimation metric MAPE deteriorates from 2.19% to 8.00% without reconstruction.
|
[] |
[] |
Temporal Generalization Estimation in Evolving Graphs
|
[
"Bin Lu",
"Tingyan Ma",
"Xiaoying Gan",
"Xinbing Wang",
"Yunqiang Zhu",
"Chenghu Zhou",
"Shiyu Liang"
] |
2404.04969
| 19,001 |
https://openreview.net/forum?id=HFtrXBfNru
|
|
[] |
Poster
|
[] |
Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly. To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation. Specifically, we propose an autoencoder that succinctly encodes a video as a combination of a content frame (like an image) and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video, respectively. We generate the content frame by fine-tuning a pretrained image diffusion model, and we generate the motion latent representation by training a new lightweight diffusion model. A key innovation here is the design of a compact latent space that can directly utilizes a pretrained image diffusion model, which has not been done in previous latent video diffusion models. This leads to considerably better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7$\times$ faster than prior approaches by generating a video of 512$\times$1024 resolution and length 16 in 3.1 seconds. Moreover, CMD achieves an FVD score of 212.7 on WebVid-10M, 27.3% better than the previous state-of-the-art of 292.4.
|
[] |
[] |
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
|
[
"Sihyun Yu",
"Weili Nie",
"De-An Huang",
"Boyi Li",
"Jinwoo Shin",
"Anima Anandkumar"
] |
2403.14148
| 18,258 |
https://openreview.net/forum?id=dQVtTdsvZH
|
|
[] |
Poster
|
[] |
We study macro motion analysis, where macro motion refers to the collection of all visually observable motions in a dynamic scene. Traditional filtering-based methods on motion analysis typically focus only on local and tiny motions, yet fail to represent large motions or 3D scenes. Recent dynamic neural representations can faithfully represent motions using correspondences, but they cannot be directly used for motion analysis. In this work, we propose Phase-based neural polynomial Gabor fields (Phase-PGF), which learns to represent scene dynamics with low-dimensional time-varying phases. We theoretically show that Phase-PGF has several properties suitable for macro motion analysis. In our experiments, we collect diverse 2D and 3D dynamic scenes and show that Phase-PGF enables dynamic scene analysis and editing tasks including motion loop detection, motion factorization, motion smoothing, and motion magnification.
|
[] |
[] |
Neural Polynomial Gabor Fields for Macro Motion Analysis
|
[
"Chen Geng",
"Hong-Xing Yu",
"Sida Peng",
"Xiaowei Zhou",
"Jiajun Wu"
] | 18,257 |
https://openreview.net/forum?id=dTlKCQuuxP
|
||
[] |
Poster
|
[] |
Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the \textit{latent space} of pretrained autoencoders as a more expressive and robust representation for motion extraction and infusion. Building upon this, we present a novel \textit{generative} model that produces diverse stylization results of a single motion (latent) code. During training, a motion code is decomposed into two coding components: a deterministic content code, and a probabilistic style code adhering to a prior distribution; then a generator massages the random combination of content and style codes to reconstruct the corresponding motion codes. Our approach is versatile, allowing the learning of probabilistic style space from either style labeled or unlabeled motions, providing notable flexibility in stylization as well. In inference, users can opt to stylize a motion using style cues from a reference motion or a label. Even in the absence of explicit style input, our model facilitates novel re-stylization by sampling from the unconditional style prior distribution. Experimental results show that our proposed stylization models, despite their lightweight design, outperform the state-of-the-arts in style reeanactment, content preservation, and generalization across various applications and settings.
|
[] |
[] |
Generative Human Motion Stylization in Latent Space
|
[
"chuan guo",
"Yuxuan Mu",
"Xinxin Zuo",
"Peng Dai",
"Youliang Yan",
"Juwei Lu",
"Li Cheng"
] |
2401.13505
| 18,255 |
https://openreview.net/forum?id=daEqXJ0yZo
|
|
[] |
Poster
|
[] |
Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As part of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the “true” reward, these learned reward models are susceptible to overoptimization. Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger “gold” reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used. Using a similar setup, we conduct a systematic study to evaluate the efficacy of using ensemble-based conservative optimization objectives, specifically worst-case optimization (WCO) and uncertainty-weighted optimization (UWO), for mitigating reward model overoptimization when using two optimization methods: (a) best-of-n sampling (BoN) (b) proximal policy optimization (PPO). We additionally extend the setup of Gao et al. (2023) to include 25% label noise to better mirror real-world conditions. Both with and without label noise we find that conservative optimization practically eliminates overoptimization and improves performance by up to 70% for BoN sampling. For PPO, ensemble-based conservative optimization always reduces overoptimization and outperforms single reward model optimization. Moreover, combining it with a small KL penalty successfully prevents overoptimization at no performance cost. Overall, our results demonstrate that ensemble-based conservative optimization can effectively counter overoptimization.
|
[] |
[] |
Reward Model Ensembles Help Mitigate Overoptimization
|
[
"Thomas Coste",
"Usman Anwar",
"Robert Kirk",
"David Krueger"
] |
2310.02743
| 18,253 |
https://openreview.net/forum?id=dcjtMYkpXx
|
|
[] |
Poster
|
[] |
Bayesian Optimization (BO) is typically used to optimize an unknown function $f$ that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorithms are efficient at optimizing low-dimensional functions, scaling them to high-dimensional spaces remains an open problem, often tackled by assuming an additive structure for $f$. By doing so, BO algorithms typically introduce additional restrictive assumptions on the additive structure that reduce their applicability domain. This paper contains two main contributions: (i) we relax the restrictive assumptions on the additive structure of $f$, at the expense of weakening the maximization guarantees of the acquisition function, and (ii) we address the over-exploration problem for decentralized BO algorithms. To these ends, we propose DumBO, an asymptotically optimal decentralized BO algorithm that achieves very competitive performance against state-of-the-art BO algorithms, especially when the additive structure of $f$ comprises high-dimensional factors.
|
[] |
[] |
Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
|
[
"Anthony Bardou",
"Patrick Thiran",
"Thomas Begin"
] |
2305.19838
| 18,252 |
https://openreview.net/forum?id=de1218PoEl
|
|
[] |
Poster
|
[
"https://github.com/eeyhsong/NICE-EEG"
] |
Electroencephalography (EEG) signals, known for the convenient non-invasive acquisition but low signal-to-noise, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes image and EEG encoders to extract features from paired image stimuli and EEG responses. Contrastive learning aligns these two modalities by constraining their similarity. With the framework, we attain significantly above-chance results on a comprehensive EEG-image dataset, achieving a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. Moreover, we perform extensive experiments to explore the biological plausibility by resolving the temporal, spatial, spectral, and semantic aspects of EEG signals. Besides, we introduce attention modules to capture spatial correlations, providing implicit evidence of the brain activity perceived from EEG data. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios.
|
[] |
[] |
Decoding Natural Images from EEG for Object Recognition
|
[
"Yonghao Song",
"Bingchuan Liu",
"Xiang Li",
"Nanlin Shi",
"Yijun Wang",
"Xiaorong Gao"
] |
2308.13234
| 18,251 |
https://openreview.net/forum?id=dhLIno8FmH
|
|
[] |
Spotlight Poster
|
[
"https://github.com/Stability-AI/generative-models"
] |
We present Stable Diffusion XL (SDXL), a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone, achieved by significantly increasing the number of attention blocks and including a second text encoder. Further, we design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. To ensure highest quality results, we also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL improves dramatically over previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators such as Midjourney.
|
[] |
[] |
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
|
[
"Dustin Podell",
"Zion English",
"Kyle Lacey",
"Andreas Blattmann",
"Tim Dockhorn",
"Jonas Müller",
"Joe Penna",
"Robin Rombach"
] |
2307.01952
| 18,250 |
https://openreview.net/forum?id=di52zR8xgf
|
|
[] |
Poster
|
[] |
Retrosynthesis is the task of proposing a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by the algorithm may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms.
|
[] |
[] |
Retro-fallback: retrosynthetic planning in an uncertain world
|
[
"Austin Tripp",
"Krzysztof Maziarz",
"Sarah Lewis",
"Marwin Segler",
"José Miguel Hernández-Lobato"
] | 18,248 |
https://openreview.net/forum?id=dl0u4ODCuW
|
||
[] |
Poster
|
[] |
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term. Building upon our interpretation, we propose a novel Noise-Free Score Distillation (NFSD) process, which requires minimal modifications to the original SDS framework. Through this streamlined design, we achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale. This strategic choice allows us to prevent the over-smoothing of results, ensuring that the generated data is both realistic and complies with the desired prompt. To demonstrate the efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as well as several other methods.
|
[] |
[] |
Noise-free Score Distillation
|
[
"Oren Katzir",
"Or Patashnik",
"Daniel Cohen-Or",
"Dani Lischinski"
] |
2310.17590
| 18,247 |
https://openreview.net/forum?id=dlIMcmlAdk
|
|
[] |
Poster
|
[] |
Feature shaping refers to a family of methods that exhibit state-of-the-art performance for out-of-distribution (OOD) detection. These approaches manipulate the feature representation, typically from the penultimate layer of a pre-trained deep learning model, so as to better differentiate between in-distribution (ID) and OOD samples. However, existing feature-shaping methods usually employ rules manually designed for specific model architectures and OOD datasets, which consequently limit their generalization ability. To address this gap, we first formulate an abstract optimization framework for studying feature-shaping methods. We then propose a concrete reduction of the framework with a simple piecewise constant shaping function and show that existing feature-shaping methods approximate the optimal solution to the concrete optimization problem. Further, assuming that OOD data is inaccessible, we propose a formulation that yields a closed-form solution for the piecewise constant shaping function, utilizing solely the ID data. Through extensive experiments, we show that the feature-shaping function optimized by our method improves the generalization ability of OOD detection across a large variety of datasets and model architectures.
|
[] |
[] |
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
|
[
"Qinyu Zhao",
"Ming Xu",
"Kartik Gupta",
"Akshay Asthana",
"Liang Zheng",
"Stephen Gould"
] |
2402.00865
| 18,246 |
https://openreview.net/forum?id=dm8e7gsH0d
|
|
[] |
Poster
|
[
"https://github.com/Westlake-AI/SemiReward"
] |
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias. However, existing pseudo-label selection strategies are limited to pre-defined schemes or complex hand-crafted policies specially designed for classification, failing to achieve high-quality labels, fast convergence, and task versatility simultaneously. To these ends, we propose a Semi-supervised Reward framework (SemiReward) that predicts reward scores to evaluate and filter out high-quality pseudo labels, which is pluggable to mainstream SSL methods in wide task types and scenarios. To mitigate confirmation bias, SemiReward is trained online in two stages with a generator model and subsampling strategy. With classification and regression tasks on 13 standard SSL benchmarks of three modalities, extensive experiments verify that SemiReward achieves significant performance gains and faster convergence speeds upon Pseudo Label, FlexMatch, and Free/SoftMatch.
|
[] |
[] |
SemiReward: A General Reward Model for Semi-supervised Learning
|
[
"Siyuan Li",
"Weiyang Jin",
"Zedong Wang",
"Fang Wu",
"Zicheng Liu",
"Cheng Tan",
"Stan Z. Li"
] |
2310.03013
| 18,245 |
https://openreview.net/forum?id=dnqPvUjyRI
|
|
[] |
Poster
|
[] |
Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency.
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[] |
[] |
Periodicity Decoupling Framework for Long-term Series Forecasting
|
[
"Tao Dai",
"Beiliang Wu",
"Peiyuan Liu",
"Naiqi Li",
"Jigang Bao",
"Yong Jiang",
"Shu-Tao Xia"
] | 18,244 |
https://openreview.net/forum?id=dp27P5HBBt
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||
[] |
Poster
|
[] |
It has been shown that deep neural networks of a large enough width are universal approximators but they are not if the width is too small.There were several attempts to characterize the minimum width $w_{\min}$ enabling the universal approximation property; however, only a few of them found the exact values.In this work, we show that the minimum width for universal approximation of $L^p$ functions from $[0,1]^{d_x}$ to $\mathbb R^{d_y}$ is exactly $\max\\{d_x,d_y,2\\}$ if an activation function is ReLU-Like (e.g., ReLU, GELU, Softplus).Compared to the known result for ReLU networks, $w_{\min}=\max\\{d_x+1,d_y\\}$ when the domain is ${\mathbb R^{d_x}}$, our result first shows that approximation on a compact domain requires smaller width than on ${\mathbb R^{d_x}}$.We next prove a lower bound on $w_{\min}$ for uniform approximation using general activation functions including ReLU: $w_{\min}\ge d_y+1$ if $d_x<d_y\le2d_x$. Together with our first result, this shows a dichotomy between $L^p$ and uniform approximations for general activation functions and input/output dimensions.
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[] |
[] |
Minimum width for universal approximation using ReLU networks on compact domain
|
[
"Namjun Kim",
"Chanho Min",
"Sejun Park"
] |
2309.10402
| 18,243 |
https://openreview.net/forum?id=dpDw5U04SU
|
|
[] |
Poster
|
[] |
Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how even small transformers, trained from random initialization, can efficiently learn arithmetic operations such as addition, multiplication, and elementary functions like square root, using the next-token prediction objective. We first demonstrate that conventional training data is not the most effective for arithmetic learning, and simple formatting changes can significantly improve accuracy. This leads to sharp phase transitions as a function of training data scale, which, in some cases, can be explained through connections to low-rank matrix completion. Building on prior work, we then train on chain-of-thought style data that includes intermediate step results. Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed. We also study the interplay between arithmetic and text data during training and examine the effects of few-shot prompting, pretraining, and parameter scaling. Additionally, we discuss the challenges associated with length generalization. Our work highlights the importance of high-quality, instructive data that considers the particular characteristics of the next-word prediction loss for rapidly eliciting arithmetic capabilities.
|
[] |
[] |
Teaching Arithmetic to Small Transformers
|
[
"Nayoung Lee",
"Kartik Sreenivasan",
"Jason D. Lee",
"Kangwook Lee",
"Dimitris Papailiopoulos"
] |
2307.03381
| 18,241 |
https://openreview.net/forum?id=dsUB4bst9S
|
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[] |
Spotlight Poster
|
[] |
Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency on sensitive private information. A practical solution is to host a local LLM and optimize a soft prompt privately using data. Yet, hosting a local model becomes problematic when model ownership is protected. Alternative methods, like sending data to the model's provider for training, intensify these privacy issues facing an untrusted provider. In this paper, we present a novel solution called Differentially-Private Offsite Prompt Tuning (DP-OPT) to address this challenge. Our approach involves tuning a discrete prompt on the client side and then applying it to the desired cloud models. We demonstrate that prompts suggested by LLMs themselves can be transferred without compromising performance significantly. To ensure that the prompts do not leak private information, we introduce the first private prompt generation mechanism, by a differentially-private (DP) ensemble of in-context learning with private demonstrations. With DP-OPT, generating privacy-preserving prompts by Vicuna-7b can yield competitive performance compared to non-private in-context learning on GPT3.5 or local private prompt tuning.Codes are available at https://github.com/VITA-Group/DP-OPT.
|
[] |
[] |
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
|
[
"Junyuan Hong",
"Jiachen T. Wang",
"Chenhui Zhang",
"Zhangheng LI",
"Bo Li",
"Zhangyang Wang"
] | 18,958 |
https://openreview.net/forum?id=Ifz3IgsEPX
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||
[] |
Poster
|
[] |
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdit attack framework. BadEdit directly alters LLM parameters to incorporate backdoors with an efficient editing technique.It boasts superiority over existing backdoor injection techniques in several areas:(1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).(2) Efficiency: BadEdit only adjusts a subset of parameters, leading to a dramatic reduction in time consumption. (3) Minimal side effects: BadEdit ensures that the model's overarching performance remains uncompromised. (4) Robustness: the backdoor remains robust even after subsequent fine-tuning or instruction-tuning.Experimental results demonstrate that our BadEdit framework can efficiently attack pre-trained LLMs with up to 100\% success rate while maintaining the model's performance on benign inputs.
|
[] |
[] |
BadEdit: Backdooring Large Language Models by Model Editing
|
[
"Yanzhou Li",
"Kangjie Chen",
"Tianlin Li",
"Jian Zhang",
"Shangqing Liu",
"Wenhan Wang",
"Tianwei Zhang",
"Yang Liu"
] |
2403.13355
| 18,240 |
https://openreview.net/forum?id=duZANm2ABX
|
|
[] |
Poster
|
[
"https://github.com/snap-research/HyperHuman"
] |
Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL·E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a large-scale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios.
|
[] |
[] |
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion
|
[
"Xian Liu",
"Jian Ren",
"Aliaksandr Siarohin",
"Ivan Skorokhodov",
"Yanyu Li",
"Dahua Lin",
"Xihui Liu",
"Ziwei Liu",
"Sergey Tulyakov"
] |
2310.08579
| 18,239 |
https://openreview.net/forum?id=duyA42HlCK
|
|
[] |
Poster
|
[] |
Lipschitz constant estimation plays an important role for understanding generalization, robustness, and fairness in deep learning. Unlike naive bounds based on the network weight norm product, semidefinite programs (SDPs) have shown great promise in providing less conservative Lipschitz bounds with polynomial-time complexity guarantees. However, due to the memory consumption and running speed, standard SDP algorithms cannot scale to modern neural network structures. In this paper, we transform the SDPs for Lipschitz constant estimation into an eigenvalue problem, which aligns with the modern large optimization paradigms based on first-order methods. This is amenable to autodiff frameworks such as PyTorch and TensorFlow, requiring significantly less memory than standard SDP algorithms. The transformation also allows us to leverage various existing numerical techniques for eigenvalue optimization, opening the way for further memory improvement and computational speedup. The essential technique of our eigenvalue-problem transformation is to introduce redundant quadratic constraints and then utilize both Lagrangian and Shor's SDP relaxations. Numerical examples demonstrate that our technique is more scalable than existing approaches. For networks that existing SDP solvers cannot handle, we improve the Lipschitz constant estimation by up to 58\% compared to the weight matrix norm product bound.
|
[] |
[] |
On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks
|
[
"Zi Wang",
"Aaron J Havens",
"Alexandre Araujo",
"Yang Zheng",
"Bin Hu",
"Yudong Chen",
"Somesh Jha"
] | 18,238 |
https://openreview.net/forum?id=dwzLn78jq7
|
||
[] |
Poster
|
[
"https://github.com/CownowAn/DiffusionNAG"
] |
Existing NAS methods suffer from either an excessive amount of time for repetitive sampling and training of many task-irrelevant architectures. To tackle such limitations of existing NAS methods, we propose a paradigm shift from NAS to a novel conditional Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. Specifically, we consider the neural architectures as directed graphs and propose a graph diffusion model for generating them. Moreover, with the guidance of parameterized predictors, DiffusionNAG can flexibly generate task-optimal architectures with the desired properties for diverse tasks, by sampling from a region that is more likely to satisfy the properties. This conditional NAG scheme is significantly more efficient than previous NAS schemes which sample the architectures and filter them using the property predictors. We validate the effectiveness of DiffusionNAG through extensive experiments in two predictor-based NAS scenarios: Transferable NAS and Bayesian Optimization (BO)-based NAS. DiffusionNAG achieves superior performance with speedups of up to 20× when compared to the baselines on Transferable NAS benchmarks. Furthermore, when integrated into a BO-based algorithm, DiffusionNAG outperforms existing BO-based NAS approaches, particularly in the large MobileNetV3 search space on the ImageNet 1K dataset.
|
[] |
[] |
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
|
[
"Sohyun An",
"Hayeon Lee",
"Jaehyeong Jo",
"Seanie Lee",
"Sung Ju Hwang"
] |
2305.16943
| 18,237 |
https://openreview.net/forum?id=dyG2oLJYyX
|
|
[] |
Spotlight Poster
|
[
"https://github.com/OscarXZQ/weight-selection"
] |
Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era.
|
[] |
[] |
Initializing Models with Larger Ones
|
[
"Zhiqiu Xu",
"Yanjie Chen",
"Kirill Vishniakov",
"Yida Yin",
"Zhiqiang Shen",
"Trevor Darrell",
"Lingjie Liu",
"Zhuang Liu"
] |
2311.18823
| 18,236 |
https://openreview.net/forum?id=dyrGMhicMw
|
|
[] |
Poster
|
[] |
Few neural architectures lend themselves to provable learning with gradient based methods. One popular model is the single-index model, in which labels are produced by composing an unknown linear projection with a possibly unknown scalar link function. Learning this model with SGD is relatively well-understood, whereby the so-called information exponent of the link function governs a polynomial sample complexity rate. However, extending this analysis to deeper or more complicated architectures remains challenging.In this work, we consider single index learning in the setting of symmetric neural networks. Under analytic assumptions on the activation and maximum degree assumptions on the link function, we prove that gradient flow recovers the hidden planted direction, represented as a finitely supported vector in the feature space of power sum polynomials. We characterize a notion of information exponent adapted to our setting that controls the efficiency of learning.
|
[] |
[] |
Symmetric Single Index Learning
|
[
"Aaron Zweig",
"Joan Bruna"
] |
2310.02117
| 18,235 |
https://openreview.net/forum?id=e1vqloonRy
|
|
[] |
Poster
|
[] |
Deep neural network (DNN) models, despite their impressive performance, are vulnerable to exploitation by attackers who attempt to adapt them to other tasks for their own benefit. Current defense strategies mainly address this vulnerability at the model parameter level, leaving the potential of architectural-level defense largely unexplored. This paper, for the first time, addresses the issue of model protection by reducing transferability at the architecture level. Specially, we present a novel neural architecture search (NAS)-enabled algorithm that employs zero-cost proxies and evolutionary search, to design model architectures with low transferability. Our method, namely ArchLock, aims to achieve high performance on the source task, while degrading the performance on target tasks, i.e., locking the transferability of a DNN model.To achieve efficient cross-task search without having access to the training data owned by the attackers, we utilize zero-cost proxies to speed up architecture evaluation and simulate potential target task embeddings to assist cross-task search with a binary performance predictor. Extensive experiments on NAS-Bench-201 and TransNAS-Bench-101 demonstrate that ArchLock reduces transferability by up to 30\% and 50%, respectively, with negligible performance degradation on source tasks (<2%).
|
[] |
[] |
ArchLock: Locking DNN Transferability at the Architecture Level with a Zero-Cost Binary Predictor
|
[
"Tong Zhou",
"Shaolei Ren",
"Xiaolin Xu"
] | 18,234 |
https://openreview.net/forum?id=e2YOVTenU9
|
||
[] |
Spotlight Poster
|
[] |
Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It achieves results comparable to AdamW but with greater memory efficiency. As what we can expect from the result of the random search, Lion blends a number of elements from existing algorithms, including signed momentum, decoupled weight decay, Polayk and Nesterov momentum, but doesn't fit into any existing category of theoretically grounded optimizers. Thus, even though Lion appears to perform well as a general-purpose optimizer for a wide range of tasks, its theoretical basis remains uncertain. This absence of theoretical clarity limits opportunities to further enhance and expand Lion's efficacy. This work aims to demystify Lion. Using both continuous-time and discrete-time analysis, we demonstrate that Lion is a novel and theoretically grounded approach for minimizing a general loss function $f(x)$ while enforcing a bound constraint $||x||_\infty \leq 1/\lambda$. Lion achieves this through the incorporation of decoupled weight decay, where $\lambda$ represents the weight decay coefficient. Our analysis is facilitated by the development of a new Lyapunov function for the Lion updates. It applies to a wide range of Lion-$\phi$ algorithms, where the $sign(\cdot)$ operator in Lion is replaced by the subgradient of a convex function $\phi$, leading to the solution of the general composite optimization problem $\min_x f(x) + \phi^*(x)$. Our findings provide valuable insights into the dynamics of Lion and pave the way for further enhancements and extensions of Lion-related algorithms.
|
[] |
[] |
Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts
|
[
"Lizhang Chen",
"Bo Liu",
"Kaizhao Liang",
"qiang liu"
] | 18,232 |
https://openreview.net/forum?id=e4xS9ZarDr
|
||
[] |
Poster
|
[
"https://github.com/OPTML-Group/DeepZero"
] |
Zeroth-order (ZO) optimization has become a popular technique for solving machine learning (ML) problems when first-order (FO) information is difficult or impossible to obtain. However, the scalability of ZO optimization remains an open problem: Its use has primarily been limited to relatively small-scale ML problems, such as sample-wise adversarial attack generation. To our best knowledge, no prior work has demonstrated the effectiveness of ZO optimization in training deep neural networks (DNNs) without a significant decrease in performance. To overcome this roadblock, we develop DeepZero, a principled and practical ZO deep learning (DL) framework that can scale ZO optimization to DNN training from scratch through three primary innovations. First, we demonstrate the advantages of coordinate-wise gradient estimation (CGE) over randomized vector-wise gradient estimation in training accuracy and computational efficiency. Second, we propose a sparsity-induced ZO training protocol that extends the model pruning methodology using only finite differences to explore and exploit the sparse DL prior in CGE. Third, we develop the methods of feature reuse and forward parallelization to advance the practical implementations of ZO training. Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time. Furthermore, we show the practical utility of DeepZero in applications of certified adversarial defense and DL-based partial differential equation error correction, achieving 10-20% improvement over SOTA. We believe our results will inspire future research on scalable ZO optimization and contribute to advancing deep learning.
|
[] |
[] |
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training
|
[
"Aochuan Chen",
"Yimeng Zhang",
"Jinghan Jia",
"James Diffenderfer",
"Konstantinos Parasyris",
"Jiancheng Liu",
"Yihua Zhang",
"Zheng Zhang",
"Bhavya Kailkhura",
"Sijia Liu"
] |
2310.02025
| 17,745 |
https://openreview.net/forum?id=qBWhjsNPEY
|
|
[
"PixArt-alpha/PixArt-XL-2-1024-MS"
] |
Spotlight Poster
|
[
"https://github.com/PixArt-alpha/PixArt-alpha"
] |
The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PixArt-$\alpha$, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PixArt-$\alpha$'s training speed markedly surpasses existing large-scale T2I models, e.g., PixArt-$\alpha$ only takes 10.8% of Stable Diffusion v1.5's training time (~675 vs. ~6,250 A100 GPU days), saving nearly \\$300,000 (\\$26,000 vs. \\$320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PixArt-$\alpha$ excels in image quality, artistry, and semantic control. We hope PixArt-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
|
[
"PixArt-alpha/PixArt-alpha"
] |
[] |
PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
|
[
"Junsong Chen",
"Jincheng YU",
"Chongjian GE",
"Lewei Yao",
"Enze Xie",
"Zhongdao Wang",
"James Kwok",
"Ping Luo",
"Huchuan Lu",
"Zhenguo Li"
] |
2310.00426
| 18,231 |
https://openreview.net/forum?id=eAKmQPe3m1
|
|
[] |
Poster
|
[] |
Neural surface reconstruction is sensitive to the camera pose noise, even when state-of-the-art pose estimators like COLMAP or ARKit are used. Existing Pose-NeRF joint optimisation methods have struggled to improve pose accuracy in challenging real-world scenarios. To overcome the challenges, we introduce the pose residual field (PoRF), a novel implicit representation that uses an MLP for regressing pose updates. Compared with the conventional per-frame pose parameter optimisation, this new representation is more robust due to parameter sharing that leverages global information over the entire sequence. Furthermore, we propose an epipolar geometry loss to enhance the supervision that leverages the correspondences exported from COLMAP results without the extra computational overhead. Our method yields promising results. On the DTU dataset, we reduce the rotation error of COLMAP poses by 78\%, leading to the reduced reconstruction Chamfer distance from 3.48mm to 0.85mm. On the MobileBrick dataset that contains casually captured unbounded 360-degree videos, our method refines ARKit poses and improves the reconstruction F1 score from 69.18 to 75.67, outperforming that with the provided ground-truth pose (75.14). These achievements demonstrate the efficacy of our approach in refining camera poses and improving the accuracy of neural surface reconstruction in real-world scenarios.
|
[] |
[] |
PORF: POSE RESIDUAL FIELD FOR ACCURATE NEURAL SURFACE RECONSTRUCTION
|
[
"Jia-Wang Bian",
"Wenjing Bian",
"Victor Adrian Prisacariu",
"Philip Torr"
] |
2310.07449
| 18,230 |
https://openreview.net/forum?id=eBeECjacpw
|
|
[] |
Poster
|
[] |
There has been a recent surge of interest in developing generally-capable agents that can adapt to new tasks without additional training in the environment. Learning world models from reward-free exploration is a promising approach, and enables policies to be trained using imagined experience for new tasks. However, achieving a general agent requires robustness across different environments. In this work, we address the novel problem of generating curricula in the reward-free setting to train robust world models. We consider robustness in terms of minimax regret over all environment instantiations and show that the minimax regret can be connected to minimising the maximum error in the world model across environment instances. This result informs our algorithm, WAKER: Weighted Acquisition of Knowledge across Environments for Robustness. WAKER selects environments for data collection based on the estimated error of the world model for each environment. Our experiments demonstrate that WAKER outperforms naı̈ve domain randomisation, resulting in improved robustness, efficiency, and generalisation.
|
[] |
[] |
Reward-Free Curricula for Training Robust World Models
|
[
"Marc Rigter",
"Minqi Jiang",
"Ingmar Posner"
] |
2306.09205
| 18,229 |
https://openreview.net/forum?id=eCGpNGDeNu
|
|
[] |
Poster
|
[] |
Context-based fine-tuning methods like prompting, in-context learning, soft prompting (prompt tuning) and prefix-tuning have gained popularity as they often match the performance of full fine-tuning with a fraction of the parameters. Despite their empirical successes, there is little theoretical understanding of how these techniques influence the internal computation of the model and their expressiveness limitations. We show that despite the continuous embedding space being much more expressive than the discrete token space, soft-prompting and prefix-tuning are strictly less expressive than full fine-tuning. Concretely, context-based fine-tuning cannot change the relative attention pattern over the content and can only bias the outputs of an attention layer in a fixed direction. While this means that fine-tuning techniques such as prompting, in-context learning, soft prompting and prefix-tuning can successfully elicit or combine skills already present in the pretrained model, they cannot learn tasks requiring new attention patterns.
|
[] |
[] |
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
|
[
"Aleksandar Petrov",
"Philip Torr",
"Adel Bibi"
] |
2310.19698
| 18,932 |
https://openreview.net/forum?id=JewzobRhay
|
|
[] |
Spotlight Poster
|
[] |
While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample efficiency, yet it is often constrained by the quality of the oracles deployed. To address the demand for robust policy improvement in real-world scenarios, we introduce a novel algorithm, Robust Policy Improvement (RPI), which actively interleaves between IL and RL based on an online estimate of their performance. RPI draws on the strengths of IL, using oracle queries to facilitate exploration—an aspect that is notably challenging in sparse-reward RL—particularly during the early stages of learning. As learning unfolds, RPI gradually transitions to RL, effectively treating the learned policy as an improved oracle. This algorithm is capable of learning from and improving upon a diverse set of black-box oracles. Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner’s performance surpasses that of the oracles in a specific state. Empirical evaluations and theoretical analysis validate that RPI excels in comparison to existing state-of-the-art methodologies, demonstrating superior performance across various benchmark domains.
|
[] |
[] |
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
|
[
"Xuefeng Liu",
"Takuma Yoneda",
"Rick Stevens",
"Matthew Walter",
"Yuxin Chen"
] |
2310.01737
| 18,227 |
https://openreview.net/forum?id=eJ0dzPJq1F
|
|
[] |
Poster
|
[] |
Kernel methods are widely used in machine learning, especially for classification problems. However, the theoretical analysis of kernel classification is still limited. This paper investigates the statistical performances of kernel classifiers. With some mild assumptions on the conditional probability $\eta(x)=\mathbb{P}(Y=1\mid X=x)$, we derive an upper bound on the classification excess risk of a kernel classifier using recent advances in the theory of kernel regression. We also obtain a minimax lower bound for Sobolev spaces, which shows the optimality of the proposed classifier. Our theoretical results can be extended to the generalization error of overparameterized neural network classifiers. To make our theoretical results more applicable in realistic settings, we also propose a simple method to estimate the interpolation smoothness of $2\eta(x)-1$ and apply the method to real datasets.
|
[] |
[] |
The optimality of kernel classifiers in Sobolev space
|
[
"Jianfa Lai",
"zhifan Li",
"Dongming Huang",
"Qian Lin"
] |
2402.01148
| 18,930 |
https://openreview.net/forum?id=JfqN3gu0i7
|
|
[] |
Poster
|
[] |
We introduce DIFFTACTILE, a physics-based and fully differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically-accurate tactile feedback. In contrast to prior tactile simulators which primarily focus on manipulating rigid bodies and often rely on simplified approximations to model stress and deformations of materials in contact, DIFFTACTILE emphasizes physics-based contact modeling with high fidelity, supporting simulations of diverse contact modes and interactions with objects possessing a wide range of material properties. Our system incorporates several key components, including a Finite Element Method (FEM) -based soft body model for simulating the sensing elastomer, a multi-material simulator for modeling diverse object types (such as elastic, plastic, cables) under manipulation, a penalty-based contact model for handling contact dynamics. The differentiable nature of our system facilitates gradient-based optimization for both 1) refining physical properties in simulation using real-world data, hence narrowing the sim-to-real gap, and 2) efficient learning of tactile-assisted grasping and contact-rich manipulation skills. Additionally, we introduce a method to infer the optical response of our tactile sensor to contact using an efficient pixel-based neural module. We anticipate that DIFFTACTILE will serve as a useful platform for studying contact-rich manipulations, leveraging the benefits of dense tactile feedback and differentiable physics. The source codes of DIFFTACTILE will be publicly available.
|
[] |
[] |
DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation
|
[
"Zilin Si",
"Gu Zhang",
"Qingwei Ben",
"Branden Romero",
"Zhou Xian",
"Chao Liu",
"Chuang Gan"
] |
2403.08716
| 18,226 |
https://openreview.net/forum?id=eJHnSg783t
|
|
[] |
Poster
|
[] |
We consider a combinatorial multi-armed bandit problem for maximum value reward function under maximum value and index feedback. This is a new feedback structure that lies in between commonly studied semi-bandit and full-bandit feedback structures. We propose an algorithm and provide a regret bound for problem instances with stochastic arm outcomes according to arbitrary distributions with finite supports. The regret analysis rests on considering an extended set of arms, associated with values and probabilities of arm outcomes, and applying a smoothness condition. Our algorithm achieves a $O((k/\Delta)\log(T))$ distribution-dependent and a $\tilde{O}(\sqrt{T})$ distribution-independent regret where $k$ is the number of arms selected in each round, $\Delta$ is a distribution-dependent reward gap and $T$ is the horizon time. Perhaps surprisingly, the regret bound is comparable to previously-known bound under more informative semi-bandit feedback. We demonstrate the effectiveness of our algorithm through experimental results.
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[] |
[] |
Combinatorial Bandits for Maximum Value Reward Function under Value-Index Feedback
|
[
"Yiliu Wang",
"Wei Chen",
"Milan Vojnovic"
] |
2305.16074
| 18,225 |
https://openreview.net/forum?id=eMHn77ZKOp
|
|
[] |
Poster
|
[] |
In the realm of reinforcement learning (RL), off-policy evaluation (OPE) holds a pivotal position, especially in high-stake human-involved scenarios such as e-learning and healthcare. Applying OPE to these domains is often challenging with scarce and underrepresentative offline training trajectories. Data augmentation has been a successful technique to enrich training data. However, directly employing existing data augmentation methods to OPE may not be feasible, due to the Markovian nature within the offline trajectories and the desire for generalizability across diverse target policies. In this work, we propose an offline trajectory augmentation approach to specifically facilitate OPE in human-involved scenarios. We propose sub-trajectory mining to extract potentially valuable sub-trajectories from offline data, and diversify the behaviors within those sub-trajectories by varying coverage of the state-action space. Our work was empirically evaluated in a wide array of environments, encompassing both simulated scenarios and real-world domains like robotic control, healthcare, and e-learning, where the training trajectories include varying levels of coverage of the state-action space. By enhancing the performance of a variety of OPE methods, our work offers a promising path forward for tackling OPE challenges in situations where data may be limited or underrepresentative.
|
[] |
[] |
On Trajectory Augmentations for Off-Policy Evaluation
|
[
"Ge Gao",
"Qitong Gao",
"Xi Yang",
"Song Ju",
"Miroslav Pajic",
"Min Chi"
] | 18,224 |
https://openreview.net/forum?id=eMNN0wIyVw
|
||
[] |
Poster
|
[
"https://github.com/SJShin-AI/UDIM"
] |
The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific domain, Sharpness-Aware Minimization (SAM) reduces the sharpness of the source domain's loss landscape. Although SAM and its variants have delivered significant improvements in DG, we highlight that there's still potential for improvement in generalizing to unknown domains through the exploration on data space. Building on this motivation, this paper introduces an objective rooted in both parameter and data perturbed regions for domain generalization, termed Unknown Domain Inconsistency Minimization (UDIM). UDIM reduces the loss landscape inconsistency between source domain and unknown domains. As unknown domains are inaccessible, these domains are empirically crafted by perturbing instances from the source domain dataset. In particular, by aligning the flat minima acquired in the source domain to the loss landscape of perturbed domains, we expect to achieve generalization grounded on these flat minima for the unknown domains. Theoretically, we validate that merging SAM optimization with the UDIM objective establishes an upper bound for the true objective of the DG task. In an empirical aspect, UDIM consistently outperforms SAM variants across multiple DG benchmark datasets. Notably, UDIM shows statistically significant improvements in scenarios with more restrictive domain information, underscoring UDIM's generalization capability in unseen domains.
|
[] |
[] |
Unknown Domain Inconsistency Minimization for Domain Generalization
|
[
"Seungjae Shin",
"HeeSun Bae",
"Byeonghu Na",
"Yoon-Yeong Kim",
"Il-chul Moon"
] |
2403.07329
| 18,223 |
https://openreview.net/forum?id=eNoiRal5xi
|
|
[] |
Poster
|
[] |
Text-to-video editing aims to edit the visual appearance of a source video conditional on textual prompts.A major challenge in this task is to ensure that all frames in the edited video are visually consistent. Most recent works apply advanced text-to-image diffusion models to this task by inflating 2D spatial attention in the U-Net into spatio-temporal attention.Although temporal context can be added through spatio-temporal attention, it may introduce some irrelevant information for each patch and therefore cause inconsistency in the edited video. In this paper, for the first time, we introduce optical flow into the attention module in diffusion model's U-Net to address the inconsistency issue for text-to-video editing.Our method, FLATTEN, enforces the patches on the same flow path across different frames to attend to each other in the attention module, thus improving the visual consistency in the edited videos. Additionally, our method is training-free and can be seamlessly integrated into any diffusion based text-to-video editing methods and improve their visual consistency.Experiment results on existing text-to-video editing benchmarks show that our proposed method achieves the new state-of-the-art performance. In particular, our method excels in maintaining the visual consistency in the edited videos.
|
[] |
[] |
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
|
[
"Yuren Cong",
"Mengmeng Xu",
"christian simon",
"Shoufa Chen",
"Jiawei Ren",
"Yanping Xie",
"Juan-Manuel Perez-Rua",
"Bodo Rosenhahn",
"Tao Xiang",
"Sen He"
] |
2310.05922
| 18,929 |
https://openreview.net/forum?id=JgqftqZQZ7
|
|
[] |
Poster
|
[
"https://github.com/mrflogs/ICLR24"
] |
Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficientfine-tuning methods, such as prompt learning and adapter, to enhance CLIP’sperformance in downstream tasks. However, these methods still require additionaltraining time and computational resources, which is undesirable for devices withlimited resources. In this paper, we revisit a classical algorithm, Gaussian Discriminant Analysis (GDA), and apply it to the downstream classification of CLIP.Typically, GDA assumes that features of each class follow Gaussian distributionswith identical covariance. By leveraging Bayes’ formula, the classifier can beexpressed in terms of the class means and covariance, which can be estimated fromthe data without the need for training. To integrate knowledge from both visual andtextual modalities, we ensemble it with the original zero-shot classifier within CLIP.Extensive results on 17 datasets validate that our method surpasses or achievescomparable results with state-of-the-art methods on few-shot classification, imbalanced learning, and out-of-distribution generalization. In addition, we extendour method to base-to-new generalization and unsupervised learning, once againdemonstrating its superiority over competing approaches.
|
[] |
[] |
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
|
[
"Zhengbo Wang",
"Jian Liang",
"Lijun Sheng",
"Ran He",
"Zilei Wang",
"Tieniu Tan"
] |
2402.04087
| 18,924 |
https://openreview.net/forum?id=Js5PJPHDyY
|
|
[] |
Poster
|
[] |
Originating from the diffusion phenomenon in physics that describes particle movement, the diffusion generative models inherit the characteristics of stochastic random walk in the data space along the denoising trajectory. However, the intrinsic mutual interference among image regions contradicts the need for practical downstream application scenarios where the preservation of low-level pixel information from given conditioning is desired (e.g., customization tasks like personalized generation and inpainting based on a user-provided single image). In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved. Notably, unlike most current methods that incorporate additional conditions by fine-tuning the base text-to-image diffusion model or learning auxiliary networks, our method provides a novel perspective to understand the task needs and is applicable to a wider range of customization scenarios in a learning-free manner. Extensive experiment results show that our proposed COW can achieve more flexible customization based on strict visual conditions in different application settings.
|
[] |
[] |
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
|
[
"Ruoyu Wang",
"Yongqi Yang",
"Zhihao Qian",
"Ye Zhu",
"Yu Wu"
] |
2306.08247
| 18,221 |
https://openreview.net/forum?id=ePOjNlOjLC
|
|
[] |
Poster
|
[] |
In standard adversarial training, models are optimized to fit invariant one-hot labels for adversarial data when the perturbations are within allowable budgets. However, the overconfident target harms generalization and causes the problem of robust overfitting. To address this issue and enhance adversarial robustness, we analyze the characteristics of robust models and identify that robust models tend to produce smoother and well-calibrated outputs. Based on the observation, we propose a simple yet effective method, Annealing Self-Distillation Rectification (ADR), which generates soft labels as a better guidance mechanism that reflects the underlying distribution of data. By utilizing ADR, we can obtain rectified labels that improve model robustness without the need for pre-trained models or extensive extra computation. Moreover, our method facilitates seamless plug-and-play integration with other adversarial training techniques by replacing the hard labels in their objectives. We demonstrate the efficacy of ADR through extensive experiments and strong performances across datasets.
|
[] |
[] |
Annealing Self-Distillation Rectification Improves Adversarial Training
|
[
"Yu-Yu Wu",
"Hung-Jui Wang",
"Shang-Tse Chen"
] |
2305.12118
| 18,220 |
https://openreview.net/forum?id=eT6oLkm1cm
|
|
[] |
Poster
|
[] |
Generating realistic time series data is important for numerous engineering and scientific applications. Several existing works tackle this problem using generative adversarial networks, however, GANs are often unstable during training and suffer from mode collpase. While variational autoencoders (VAEs) are more robust to the above issues, surprisingly, they are considered less for time series generation. In this work, we introduce Koopman VAE (KVAE), a new generative framework that is based on a novel design for the model prior, and that can be optimized for either regular and irregular training data. Inspired by the Koopman theory, we represent the latent conditional prior dynamics using a linear map. Our approach enhances generative modeling with two desired features: (i) incorporating domain knowledge can be achieved by leverageing spectral tools that prescribe constraints on the eigenvalues of the linear map; and (ii) studying the qualitative behavior and stablity of the system can be performed using tools from dynamical systems theory. Our results show that KVAE outperforms state-of-the-art GAN and VAE methods across several challenging synthetic and real-world time series generation benchmarks. Whether trained on regular or irregular data, KVAE generates time series that improve both discriminative and predictive metrics. Further, we present visual evidence suggesting that KVAE learns probability density functions that better approximate the empirical ground truth distribution.
|
[] |
[] |
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
|
[
"Ilan Naiman",
"N. Benjamin Erichson",
"Pu Ren",
"Michael W. Mahoney",
"Omri Azencot"
] |
2310.02619
| 18,218 |
https://openreview.net/forum?id=eY7sLb0dVF
|
|
[] |
Poster
|
[] |
This paper introduces a framework for formally establishing a connection between a portion of an algebraic language and a Graph Neural Network (GNN). The framework leverages Context-Free Grammars (CFG) to organize algebraic operations into generative rules that can be translated into a GNN layer model. As CFGs derived directly from a language tend to contain redundancies in their rules and variables, we present a grammar reduction scheme. By applying this strategy, we define a CFG that conforms to the third-order Weisfeiler-Lehman (3-WL) test using the matricial language MATLANG. From this 3-WL CFG, we derive a GNN model, named G$^2$N$^2$, which is provably 3-WL compliant. Through various experiments, we demonstrate the superior efficiency of G$^2$N$^2$ compared to other 3-WL GNNs across numerous downstream tasks. Specifically, one experiment highlights the benefits of grammar reduction within our framework.
|
[] |
[] |
G$^2$N$^2$ : Weisfeiler and Lehman go grammatical
|
[
"Jason Piquenot",
"Aldo Moscatelli",
"Maxime Berar",
"Pierre Héroux",
"Romain Raveaux",
"Jean-Yves RAMEL",
"Sébastien Adam"
] | 18,217 |
https://openreview.net/forum?id=eZneJ55mRO
|
||
[] |
Poster
|
[] |
The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data.In this work, we study data-driven offline training for web agents with vision-language foundation models.We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages and outputs web navigation actions, such as click and type.WebGUM is trained by jointly finetuning an instruction-finetuned language model and a vision encoder with temporal and local perception on a large corpus of demonstrations.We empirically demonstrate this recipe improves the agent's ability of grounded multimodal perception, HTML comprehension, and multi-step reasoning, outperforming prior works by a significant margin. On the MiniWoB, we improve over the previous best offline methods by more than 45.8%, even outperforming online-finetuned SoTA, humans, and GPT-4-based agent. On the WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing SoTA, PaLM-540B.Furthermore, WebGUM exhibits strong positive transfer to the real-world planning tasks on the Mind2Web.We also collect 347K high-quality demonstrations using our trained models, 38 times larger than prior work, and make them available to promote future research in this direction.
|
[] |
[] |
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
|
[
"Hiroki Furuta",
"Kuang-Huei Lee",
"Ofir Nachum",
"Yutaka Matsuo",
"Aleksandra Faust",
"Shixiang Shane Gu",
"Izzeddin Gur"
] |
2305.11854
| 18,215 |
https://openreview.net/forum?id=efFmBWioSc
|
|
[] |
Poster
|
[] |
3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employing category-specific skeletal models. Consequently, they do not generalize well to articulated objects in the wild. We treat an articulated object as an unknown, semi-rigid skeletal structure surrounded by nonrigid material (e.g., skin). Our method simultaneously estimates the visible (explicit) representation (3D shapes, colors, camera parameters) and the underlying (implicit) skeletal representation, from motion cues in the object video without 3D supervision. Our implicit representation consists of four parts. (1) skeleton, which specifies which semi-rigid parts are connected. (2) Semi-rigid Part Assignment, which associates each surface vertex with a semi-rigid part. (3) Rigidity Coefficients, specifying the articulation of the local surface. (4) Time-Varying Transformations, which specify the skeletal motion and surface deformation parameters. We introduce an algorithm that uses these constraints as regularization terms and iteratively estimates both implicit and explicit representations. Our method is category-agnostic, thus eliminating the need for category-specific skeletons, we show that our method outperforms state-of-the-art across standard video datasets.
|
[] |
[] |
Learning Implicit Representation for Reconstructing Articulated Objects
|
[
"Hao Zhang",
"Fang Li",
"Samyak Rawlekar",
"Narendra Ahuja"
] |
2401.08809
| 18,906 |
https://openreview.net/forum?id=KQ2i6jazVK
|
|
[] |
Poster
|
[] |
Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments. Extracting effective spatiotemporal relationships among traffic elements is key to accurate forecasting. Inspired by the successful practice of pretrained large language models, this paper presents SEPT, a modeling framework that leverages self-supervised learning to develop powerful spatiotemporal understanding for complex traffic scenes. Specifically, our approach involves three masking-reconstruction modeling tasks on scene inputs including agents' trajectories and road network, pretraining the scene encoder to capture kinematics within trajectory, spatial structure of road network, and interactions among roads and agents. The pretrained encoder is then finetuned on the downstream forecasting task. Extensive experiments demonstrate that SEPT, without elaborate architectural design or manual feature engineering, achieves state-of-the-art performance on the Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming previous methods on all main metrics by a large margin.
|
[] |
[] |
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
|
[
"Zhiqian Lan",
"Yuxuan Jiang",
"Yao Mu",
"Chen Chen",
"Shengbo Eben Li"
] |
2309.15289
| 18,214 |
https://openreview.net/forum?id=efeBC1sQj9
|
|
[] |
Poster
|
[] |
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive *metrizable conditions*, sufficient conditions for the discriminator to serve as the distance between the distributions, by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme called the Slicing Adversarial Network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the effectiveness of SAN as compared to the usual GANs. We also apply SAN to StyleGAN-XL, which leads to a state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256$\times$256.
|
[] |
[] |
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
|
[
"Yuhta Takida",
"Masaaki Imaizumi",
"Takashi Shibuya",
"Chieh-Hsin Lai",
"Toshimitsu Uesaka",
"Naoki Murata",
"Yuki Mitsufuji"
] |
2301.12811
| 18,212 |
https://openreview.net/forum?id=eiF7TU1E8E
|
|
[] |
Poster
|
[] |
The structure learning problem consists of fitting data generated by a Directed Acyclic Graph (DAG) to correctly reconstruct its arcs. In this context, differentiable approaches constrain or regularize an optimization problem with a continuous relaxation of the acyclicity property. The computational cost of evaluating graph acyclicity is cubic on the number of nodes and significantly affects scalability. In this paper, we introduce COSMO, a constraint-free continuous optimization scheme for acyclic structure learning. At the core of our method lies a novel differentiable approximation of an orientation matrix parameterized by a single priority vector. Differently from previous works, our parameterization fits a smooth orientation matrix and the resulting acyclic adjacency matrix without evaluating acyclicity at any step. Despite this absence, we prove that COSMO always converges to an acyclic solution. In addition to being asymptotically faster, our empirical analysis highlights how COSMO performance on graph reconstruction compares favorably with competing structure learning methods.
|
[] |
[] |
Constraint-Free Structure Learning with Smooth Acyclic Orientations
|
[
"Riccardo Massidda",
"Francesco Landolfi",
"Martina Cinquini",
"Davide Bacciu"
] |
2309.08406
| 18,899 |
https://openreview.net/forum?id=KWO8LSUC5W
|
|
[] |
Spotlight Poster
|
[] |
Graph Anomaly Detection (GAD) has surfaced as a significant field of research, predominantly due to its substantial influence in production environments. Although existing approaches for node anomaly detection have shown effectiveness, they have yet to fully address two major challenges: operating in settings with limited supervision and managing class imbalance effectively. In response to these challenges, we propose a novel model, ConsisGAD, which is tailored for GAD in scenarios characterized by limited supervision and is anchored in the principles of consistency training. Under limited supervision, ConsisGAD effectively leverages the abundance of unlabeled data for consistency training by incorporating a novel learnable data augmentation mechanism, thereby introducing controlled noise into the dataset. Moreover, ConsisGAD takes advantage of the variance in homophily distribution between normal and anomalous nodes to craft a simplified GNN backbone, enhancing its capability to distinguish effectively between these two classes. Comprehensive experiments on several benchmark datasets validate the superior performance of ConsisGAD in comparison to state-of-the-art baselines.
|
[] |
[] |
Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision
|
[
"Nan Chen",
"Zemin Liu",
"Bryan Hooi",
"Bingsheng He",
"Rizal Fathony",
"Jun Hu",
"Jia Chen"
] | 18,209 |
https://openreview.net/forum?id=elMKXvhhQ9
|
||
[] |
Poster
|
[] |
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning agent that utilizes spatial and temporal abstractions to generalize learned skills in novel situations. It automatically decomposes the task at hand into smaller-scale, more manageable subtasks and hence enables sparse decision-making and focuses its computation on the relevant parts of the environment. This relies on the definition of a high-level proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end using hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper’s significant advantage in zero-shot generalization, compared to existing state-of-the-art hierarchical planning methods.
|
[] |
[] |
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
|
[
"Mingde Zhao",
"Safa Alver",
"Harm van Seijen",
"Romain Laroche",
"Doina Precup",
"Yoshua Bengio"
] |
2310.00229
| 18,208 |
https://openreview.net/forum?id=eo9dHwtTFt
|
|
[] |
Spotlight Poster
|
[] |
Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have demonstrated the impressive performance of deep SNNs on challenging tasks, their energy efficiency advantage has been diminished. Existing methods targeting energy consumption reduction do not fully exploit sparsity, whereas powerful pruning methods can achieve high sparsity but are not directly targeted at energy efficiency, limiting their effectiveness in energy saving. Furthermore, none of these works fully exploit the sparsity of neurons or the potential for unstructured neuron pruning in SNNs. In this paper, we propose a novel pruning framework that combines unstructured weight pruning with unstructured neuron pruning to maximize the utilization of the sparsity of neuromorphic computing, thereby enhancing energy efficiency. To the best of our knowledge, this is the first application of unstructured neuron pruning to deep SNNs. Experimental results demonstrate that our method achieves impressive energy efficiency gains. The sparse network pruned by our method with only 0.63\% remaining connections can achieve a remarkable 91 times increase in energy efficiency compared to the original dense network, requiring only 8.5M SOPs for inference, with merely 2.19\% accuracy loss on the CIFAR-10 dataset. Our work suggests that deep and dense SNNs exhibit high redundancy in energy consumption, highlighting the potential for targeted SNN sparsification to save energy.
|
[] |
[] |
Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework
|
[
"Xinyu Shi",
"Jianhao Ding",
"Zecheng Hao",
"Zhaofei Yu"
] | 18,207 |
https://openreview.net/forum?id=eoSeaK4QJo
|
||
[] |
Poster
|
[] |
A key challenge of modern machine learning systems is to achieve Out-of-Distribution (OOD) generalization --- generalizing to target data whose distribution differs from those of source data. Despite its significant importance, the fundamental question of ``what are the most effective algorithms for OOD generalization'' remains open even under the standard setting of covariate shift.This paper addresses this fundamental question by proving that, surprisingly, classical Maximum Likelihood Estimation (MLE) purely using source data (without any modification) achieves the *minimax* optimality for covariate shift under the *well-specified* setting. This result holds for a very large class of parametric models, including but not limited to linear regression, logistic regression, and phase retrieval, and does not require any boundedness condition on the density ratio. This paper further complement the study by proving that for the *misspecified setting*, MLE can perform poorly, and the Maximum Weighted Likelihood Estimator (MWLE) emerges as minimax optimal in specific scenarios, outperforming MLE.
|
[] |
[] |
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
|
[
"Jiawei Ge",
"Shange Tang",
"Jianqing Fan",
"Cong Ma",
"Chi Jin"
] |
2311.15961
| 18,206 |
https://openreview.net/forum?id=eoTCKKOgIs
|
|
[] |
Spotlight Poster
|
[
"https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining"
] |
In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic unified understanding between symbolic equations and their numeric counterparts. To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training, which employs joint contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the pre-trained embeddings. By performing latent space analysis, we observe that SNIP provides cross-domain insights into the representations, revealing that symbolic supervision enhances the embeddings of numeric data and vice versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric mathematical property prediction and numeric-to-symbolic equation discovery, commonly known as symbolic regression. Results show that SNIP effectively transfers to various tasks, consistently outperforming fully supervised baselines and competing strongly with established task-specific methods, especially in few-shot learning scenarios where available data is limited.
|
[] |
[] |
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
|
[
"Kazem Meidani",
"Parshin Shojaee",
"Chandan K. Reddy",
"Amir Barati Farimani"
] |
2310.02227
| 18,896 |
https://openreview.net/forum?id=KZSEgJGPxu
|
|
[] |
Poster
|
[] |
Text-conditioned diffusion models have emerged as a promising tool for neural video generation. However, current models still struggle with intricate spatiotemporal prompts and often generate restricted or incorrect motion (e.g., even lacking the ability to be prompted for objects moving from left to right). To address these limitations, we introduce LLM-grounded Video Diffusion (LVD). Instead of directly generating videos from the text inputs, LVD first leverages a large language model (LLM) to generate dynamic scene layouts based on the text inputs and subsequently uses the generated layouts to guide a diffusion model for video generation. We show that LLMs are able to understand complex spatiotemporal dynamics from text alone and generate layouts that align closely with both the prompts and the object motion patterns typically observed in the real world. We then propose to guide video diffusion models with these layouts by adjusting the attention maps. Our approach is training-free and can be integrated into any video diffusion model that admits classifier guidance. Our results demonstrate that LVD significantly outperforms its base video diffusion model and several strong baseline methods in faithfully generating videos with the desired attributes and motion patterns.
|
[] |
[] |
LLM-grounded Video Diffusion Models
|
[
"Long Lian",
"Baifeng Shi",
"Adam Yala",
"Trevor Darrell",
"Boyi Li"
] |
2309.17444
| 18,205 |
https://openreview.net/forum?id=exKHibougU
|
|
[] |
Spotlight Poster
|
[] |
Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and serving. Due to a lack of test node labels and unknown potential training-test graph data distribution shifts, conventional model evaluation encounters limitations in calculating performance metrics (e.g., test error) and measuring graph data-level discrepancies, particularly when the training graph used for developing GNNs remains unobserved during test time.In this paper, we study a new research problem, online GNN evaluation, which aims to provide valuable insights into the well-trained GNNs's ability to effectively generalize to real-world unlabeled graphs under the test-time graph distribution shifts.Concretely, we develop an effective learning behavior discrepancy score, dubbed LeBeD, to estimate the test-time generalization errors of well-trained GNN models. Through a novel GNN re-training strategy with a parameter-free optimality criterion, the proposed LeBeD comprehensively integrates learning behavior discrepancies from both node prediction and structure reconstruction perspectives.This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.Extensive experiments on real-world test graphs under diverse graph distribution shifts could verify the effectiveness of the proposed method, revealing its strong correlation with ground-truth test errors on various well-trained GNN models.
|
[] |
[] |
Online GNN Evaluation Under Test-time Graph Distribution Shifts
|
[
"Xin Zheng",
"Dongjin Song",
"Qingsong Wen",
"Bo Du",
"Shirui Pan"
] |
2403.09953
| 18,895 |
https://openreview.net/forum?id=KbetDM33YG
|
|
[] |
Poster
|
[] |
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT). PT stands out from other PEFT approaches because it maintains competitive performance with fewer trainable parameters and does not drastically scale up its parameters as the model size expands. However, PT introduces additional soft prompt tokens, leading to longer input sequences, which significantly impacts training and inference time and memory usage due to the Transformer's quadratic complexity. Particularly concerning for Large Language Models (LLMs) that face heavy daily querying. To address this issue, we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt into a shorter soft prompt and a pair of low-rank matrices that are then optimised with two different learning rates. This allows DePT to achieve better performance while saving substantial memory and time costs compared to vanilla PT and its variants, without changing trainable parameter sizes. Through extensive experiments on 23 natural language processing (NLP) and vision-language (VL) tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches, including the full fine-tuning baseline, in some scenarios. Additionally, we empirically show that DEPT grows more efficient as the model size increases. Our further study reveals that DePT integrates seamlessly with parameter-efficient transfer learning in the few-shot learning setting and highlights its adaptability to various model architectures and sizes.
|
[] |
[] |
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
|
[
"Zhengxiang Shi",
"Aldo Lipani"
] |
2309.05173
| 18,890 |
https://openreview.net/forum?id=KjegfPGRde
|
|
[] |
Poster
|
[] |
Although graph neural networks have exhibited remarkable performance in various graph tasks, a significant concern is their vulnerability to adversarial attacks. Consequently, many defense methods have been proposed to alleviate the deleterious effects of adversarial attacks and learn robust graph representations. However, most of them are difficult to *simultaneously* avoid two major limitations: (i) an emergent and severe degradation in robustness when exposed to very intense attacks, and (ii) heavy computation complexity hinders them from scaling to large graphs. In response to these challenges, we introduce an innovative graph defense method for unpredictable real-world scenarios by *designing a graph robust learning framework that is resistant to robustness degradation* and *refraining from unscalable designs with heavy computation*: specifically, our method employs a denoising module, which eliminates edges that are associated with attacked nodes to reconstruct a cleaner graph; Then, it applies Mixture-of-Experts to select differentially private noises with varying magnitudes to counteract the hidden features attacked at different intensities toward robust predictions; Moreover, our overall design avoids the reliance on heavy adjacency matrix computations, such as SVD, thus facilitating its applicability even on large graphs. Comprehensive experiments have been conducted to demonstrate the anti-degraded robustness and scalability of our method, as compared to popular graph adversarial learning methods, under diverse attack intensities and various datasets of different sizes.
|
[] |
[] |
Mitigating Emergent Robustness Degradation while Scaling Graph Learning
|
[
"Xiangchi Yuan",
"Chunhui Zhang",
"Yijun Tian",
"Yanfang Ye",
"Chuxu Zhang"
] | 18,886 |
https://openreview.net/forum?id=Koh0i2u8qX
|
||
[] |
Poster
|
[] |
It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the multi-view inconsistency problem. We find that this problem primarily stems from geometric inconsistency, and avoiding misplaced geometric structures substantially mitigates the problem in the final outputs. Therefore, we improve the consistency by aligning the 2D geometric priors in diffusion models with well-defined 3D shapes during the lifting, addressing the vast majority of the problem. This is achieved by fine-tuning the 2D diffusion model to be viewpoint-aware and to produce view-specific coordinate maps of canonically oriented 3D objects. In our process, only coarse 3D information is used for aligning. This “coarse” alignment not only resolves the multi-view inconsistency in geometries but also retains the ability in 2D diffusion models to generate detailed and diversified high-quality ob-jects unseen in the 3D datasets. Furthermore, our aligned geometric priors (AGP) are generic and can be seamlessly integrated into various state-of-the-art pipelines, obtaining high generalizability in terms of unseen shapes and visual appearance while greatly alleviating the multi-view inconsistency problem. Our method represents a new state-of-the-art performance with a 85+% consistency rate by human evaluation, while many previous methods are around 30%.
|
[] |
[] |
SweetDreamer: Aligning Geometric Priors in 2D diffusion for Consistent Text-to-3D
|
[
"Weiyu Li",
"Rui Chen",
"Xuelin Chen",
"Ping Tan"
] |
2310.02596
| 18,204 |
https://openreview.net/forum?id=extpNXo6hB
|
|
[] |
Poster
|
[] |
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sampled multiple times to obtain a diverse set incurring in a cost that is orthogonal to sampling time. We tackle the question of how to improve diversity and sample efficiency by moving beyond the common assumption of independent samples. For this we propose particle guidance, an extension of diffusion-based generative sampling where a joint-particle time-evolving potential enforces diversity. We analyze theoretically the joint distribution that particle guidance generates, its implications on the choice of potential, and the connections with methods in other disciplines. Empirically, we test the framework both in the setting of conditional image generation, where we are able to increase diversity without affecting quality, and molecular conformer generation, where we reduce the state-of-the-art median error by 13% on average.
|
[] |
[] |
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
|
[
"Gabriele Corso",
"Yilun Xu",
"Valentin De Bortoli",
"Regina Barzilay",
"Tommi S. Jaakkola"
] | 18,884 |
https://openreview.net/forum?id=KqbCvIFBY7
|
||
[] |
Poster
|
[] |
We theoretically explore the relationship between sample-efficiency and adaptivity in reinforcement learning. An algorithm is sample-efficient if it uses a number of queries $n$ to the environment that is polynomial in the dimension $d$ of the problem. Adaptivity refers to the frequency at which queries are sent and feedback is processed to update the querying strategy. To investigate this interplay, we employ a learning framework that allows sending queries in $K$ batches, with feedback being processed and queries updated after each batch. This model encompasses the whole adaptivity spectrum, ranging from non-adaptive `offline' ($K=1$) to fully adaptive ($K=n$) scenarios, and regimes in between. For the problems of policy evaluation and best-policy identification under $d$-dimensional linear function approximation, we establish $\Omega(\log \log d)$ lower bounds on the number of batches $K$ required for sample-efficient algorithms with $n = O(poly(d))$ queries. Our results show that just having adaptivity ($K>1$) does not necessarily guarantee sample-efficiency. Notably, the adaptivity-boundary for sample-efficiency is not between offline reinforcement learning ($K=1$), where sample-efficiency was known to not be possible, and adaptive settings. Instead, the boundary lies between different regimes of adaptivity and depends on the problem dimension.
|
[] |
[] |
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
|
[
"Emmeran Johnson",
"Ciara Pike-Burke",
"Patrick Rebeschini"
] |
2310.01616
| 18,203 |
https://openreview.net/forum?id=ey3GhWXQ97
|
|
[] |
Spotlight Poster
|
[
"https://github.com/tyxsspa/AnyText"
] |
Diffusion model based Text-to-Image has achieved impressive achievements recently. Although current technology for synthesizing images is highly advanced and capable of generating images with high fidelity, it is still possible to give the show away when focusing on the text area in the generated image, as synthesized text often contains blurred, unreadable, or incorrect characters, making visual text generation one of the most challenging issues in this field. To address this issue, we introduce AnyText, a diffusion-based multilingual visual text generation and editing model, that focuses on rendering accurate and coherent text in the image. AnyText comprises a diffusion pipeline with two primary elements: an auxiliary latent module and a text embedding module. The former uses inputs like text glyph, position, and masked image to generate latent features for text generation or editing. The latter employs an OCR model for encoding stroke data as embeddings, which blend with image caption embeddings from the tokenizer to generate texts that seamlessly integrate with the background. We employed text-control diffusion loss and text perceptual loss for training to further enhance writing accuracy. AnyText can write characters in multiple languages, to the best of our knowledge, this is the first work to address multilingual visual text generation. It is worth mentioning that AnyText can be plugged into existing diffusion models from the community for rendering or editing text accurately. After conducting extensive evaluation experiments, our method has outperformed all other approaches by a significant margin. Additionally, we contribute the first large-scale multilingual text images dataset, AnyWord-3M, containing 3 million image-text pairs with OCR annotations in multiple languages. Based on AnyWord-3M dataset, we propose AnyText-benchmark for the evaluation of visual text generation accuracy and quality. Our project will be open-sourced soon to improve and promote the development of text generation technology.
|
[
"modelscope/AnyText"
] |
[] |
AnyText: Multilingual Visual Text Generation and Editing
|
[
"Yuxiang Tuo",
"Wangmeng Xiang",
"Jun-Yan He",
"Yifeng Geng",
"Xuansong Xie"
] |
2311.03054
| 18,202 |
https://openreview.net/forum?id=ezBH9WE9s2
|
|
[] |
Poster
|
[] |
The Segment-Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantage diminishes when applied to specialized domains like medical imagery and remote sensing. To address this limitation, this paper introduces Conv-LoRA, a simple yet effective parameter-efficient fine-tuning approach. By integrating ultra-lightweight convolutional parameters into LoRA, Conv-LoRA can inject image-related inductive biases into the plain ViT encoder, further reinforcing SAM’s local prior assumption. Notably, Conv-LoRA not only preserves SAM’s extensive segmentation knowledge but also revives its capacity of learning high-level image semantics, which is constrained by SAM’s foreground-background segmentation pretraining. Comprehensive experimentation across diverse benchmarks spanning multiple domains underscores Conv-LoRA’s superiority in adapting SAM to real-world semantic segmentation tasks.
|
[] |
[] |
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
|
[
"Zihan Zhong",
"Zhiqiang Tang",
"Tong He",
"Haoyang Fang",
"Chun Yuan"
] |
2401.17868
| 18,201 |
https://openreview.net/forum?id=ezscMer8L0
|
|
[] |
Poster
|
[] |
Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose self-debugging, which teaches a large language model to debug its predicted program. In particular, we demonstrate that self-debugging can teach the large language model to perform rubber duck debugging; i.e., without any human feedback on the code correctness or error messages, the model is able to identify its mistakes by leveraging code execution and explaining the generated code in natural language. Self-debugging achieves the state-of-the-art performance on several code generation benchmarks, including the Spider dataset for text-to-SQL generation, TransCoder for C++-to-Python translation, and MBPP for text-to-Python generation. On the Spider benchmark where there are no unit tests to verify the correctness of predictions, self-debugging with code explanation consistently improves the baseline by 2-3%, and improves the prediction accuracy on problems of the hardest level by 9%. On TransCoder and MBPP where unit tests are available, self-debugging improves the baseline accuracy by up to 12%. Meanwhile, by leveraging feedback messages and reusing failed predictions, self-debugging notably improves sample efficiency, and can match or outperform baseline models that generate more than 10$\times$ candidate programs.
|
[] |
[] |
Teaching Large Language Models to Self-Debug
|
[
"Xinyun Chen",
"Maxwell Lin",
"Nathanael Schärli",
"Denny Zhou"
] |
2304.05128
| 18,880 |
https://openreview.net/forum?id=KuPixIqPiq
|
|
[] |
Poster
|
[] |
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
|
[] |
[] |
Cycle Consistency Driven Object Discovery
|
[
"Aniket Rajiv Didolkar",
"Anirudh Goyal",
"Yoshua Bengio"
] |
2306.02204
| 18,200 |
https://openreview.net/forum?id=f1xnBr4WD6
|
|
[] |
Poster
|
[
"https://github.com/RERV/UniAdapter"
] |
Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the standard full fine-tuning paradigm becomes unsustainable due to heavy computational and storage costs. This paper proposes UniAdapter, which unifies unimodal and multimodal adapters for parameter-efficient cross-modal adaptation on pre-trained vision-language models. Specifically, adapters are distributed to different modalities and their interactions, with the total number of tunable parameters reduced by partial weight sharing. The unified and knowledge-sharing design enables powerful cross-modal representations that can benefit various downstream tasks, requiring only 1.0%-2.0% tunable parameters of the pre-trained model. Extensive experiments on 7 cross-modal downstream benchmarks (including video-text retrieval, image-text retrieval, VideoQA, VQA and Caption) show that in most cases, UniAdapter not only outperforms the state-of-the-arts, but even beats the full fine-tuning strategy. Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49.7% recall@1 with 2.2% model parameters, outperforming the latest competitors by 2.0%. The code and models are available at https://github.com/UniAdapter/UniAdapter.
|
[] |
[] |
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
|
[
"Haoyu Lu",
"Yuqi Huo",
"Guoxing Yang",
"Zhiwu Lu",
"Wei Zhan",
"Masayoshi Tomizuka",
"Mingyu Ding"
] |
2302.06605
| 18,198 |
https://openreview.net/forum?id=f5H8WGLQm5
|
|
[] |
Poster
|
[
"https://github.com/ZhentingWang/DIAGNOSIS"
] |
Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized data usage during the training or fine-tuning process. One example is when a model trainer collects a set of images created by a particular artist and attempts to train a model capable of generating similar images without obtaining permission and giving credit to the artist. To address this issue, we propose a method for detecting such unauthorized data usage by planting the injected memorization into the text-to-image diffusion models trained on the protected dataset. Specifically, we modify the protected images by adding unique contents on these images using stealthy image warping functions that are nearly imperceptible to human but can be captured and memorized by diffusion models. By analyzing whether the model has memorized the injected content (i.e., whether the generated images are processed by the injected post-processing function), we can detect models that had illegally utilized the unauthorized data. Experiments on Stable Diffusion and VQ Diffusion with different model training or fine-tuning methods (i.e, LoRA, DreamBooth, and standard training) demonstrate the effectiveness of our proposed method in detecting unauthorized data usages.
|
[] |
[] |
DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models
|
[
"Zhenting Wang",
"Chen Chen",
"Lingjuan Lyu",
"Dimitris N. Metaxas",
"Shiqing Ma"
] |
2307.03108
| 18,196 |
https://openreview.net/forum?id=f8S3aLm0Vp
|
|
[] |
Spotlight Poster
|
[] |
While instruction-tuned language models have demonstrated impressive zero-shot generalization, these models often struggle to generate accurate responses when faced with instructions that fall outside their training set. This paper presents Instructive Decoding (ID), a simple yet effective approach that augments the efficacy of instruction-tuned models. Specifically, ID adjusts the logits for next-token prediction in a contrastive manner, utilizing predictions generated from a manipulated version of the original instruction, referred to as a noisy instruction. This noisy instruction aims to elicit responses that could diverge from the intended instruction yet remain plausible. We conduct experiments across a spectrum of such noisy instructions, ranging from those that insert semantic noise via random words to others like 'opposite' that elicit the deviated responses. Our approach achieves considerable performance gains across various instruction-tuned models and tasks without necessitating any additional parameter updates. Notably, utilizing 'opposite' as the noisy instruction in ID, which shows the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.
|
[] |
[] |
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
|
[
"Taehyeon Kim",
"Joonkee Kim",
"Gihun Lee",
"Se-Young Yun"
] |
2311.00233
| 18,856 |
https://openreview.net/forum?id=LebzzClHYw
|
|
[] |
Poster
|
[] |
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equipping it with valuable semantic cues. To tackle challenges arising from color variations and selection bias, we incorporate color as "context" by providing ground-truth colors as hints during colorization.Experimental results on the KITTI and Waymo datasets demonstrate GPC's remarkable effectiveness. Even with limited labeled data, GPC significantly improves fine-tuning performance; notably, on just 20% of the KITTI dataset, GPC outperforms training from scratch with the entire dataset. In sum, we introduce a fresh perspective on pre-training for 3D object detection, aligning the objective with the model's intended role and ultimately advancing the accuracy and efficiency of 3D object detection for autonomous vehicles.
|
[] |
[] |
Pre-training LiDAR-based 3D Object Detectors through Colorization
|
[
"Tai-Yu Pan",
"Chenyang Ma",
"Tianle Chen",
"Cheng Perng Phoo",
"Katie Z Luo",
"Yurong You",
"Mark Campbell",
"Kilian Q Weinberger",
"Bharath Hariharan",
"Wei-Lun Chao"
] |
2310.14592
| 18,195 |
https://openreview.net/forum?id=fB1iiH9xo7
|
|
[] |
Poster
|
[] |
With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards developing models that satisfy robustness, safety and fairness requirements. Imposing these requirements leads to constrained learning problems, which can be tackled with dual ascent methods. However, convergence guarantees for dual ascent algorithms typically involve a randomized or averaged sequence of primal iterates. These solutions are impractical, since they require storing an ever growing sequence of models. Although it has been observed that final iterates perform well in practice, theoretical guarantees for their optimality and feasibility have remained elusive. In this work, we characterize the infeasibility of Lagrangian minimizers associated with optimal dual variables, which leads to a sub-optimality bound for best primal iterates. To do this, we leverage the fact that constrained learning problems are parametrized versions of convex functional programs. This bound sheds light on how the richness of the parametrization and the curvature of the objective impact the convergence of primal iterates. We empirically validate this finding in learning problems with fairness constraints.
|
[] |
[] |
Near-Optimal Solutions of Constrained Learning Problems
|
[
"Juan Elenter",
"Luiz F. O. Chamon",
"Alejandro Ribeiro"
] |
2403.11844
| 18,193 |
https://openreview.net/forum?id=fDaLmkdSKU
|
|
[] |
Poster
|
[] |
Zero-shot inference is a powerful paradigm that enables the use of large pretrained models for downstream classification tasks without further training. However, these models are vulnerable to inherited biases that can impact their performance. The traditional solution is fine-tuning, but this undermines the key advantage of pretrained models, which is their ability to be used out-of-the-box. We propose RoboShot, a method that improves the robustness of pretrained model embeddings in a fully zero-shot fashion. First, we use language models (LMs) to obtain useful insights from task descriptions. These insights are embedded and used to remove harmful and boost useful components in embeddings---without any supervision. Theoretically, we provide a simple and tractable model for biases in zero-shot embeddings and give a result characterizing under what conditions our approach can boost performance. Empirically, we evaluate RoboShot on nine image and NLP classification tasks and show an average improvement of 15.98% over several zero-shot baselines. Additionally, we demonstrate that RoboShot is compatible with a variety of pretrained and language models and propose a way to further boost performance with a zero-shot adaptation variant.
|
[] |
[] |
Zero-Shot Robustification of Zero-Shot Models
|
[
"Dyah Adila",
"Changho Shin",
"Linrong Cai",
"Frederic Sala"
] |
2309.04344
| 18,194 |
https://openreview.net/forum?id=fCeUoDr9Tq
|
|
[] |
Poster
|
[] |
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications. This work explores a promising direction for graph contrastive learning (GCL) with spiking neural networks (SNNs), which leverage sparse and binary characteristics to learn more biologically plausible and compact representations. We propose SpikeGCL, a novel GCL framework to learn binarized 1-bit representations for graphs, making balanced trade-offs between efficiency and performance. We provide theoretical guarantees to demonstrate that SpikeGCL has comparable expressiveness with its full-precision counterparts. Experimental results demonstrate that, with nearly 32x representation storage compression, SpikeGCL is either comparable to or outperforms many fancy state-of-the-art supervised and self-supervised methods across several graph benchmarks.
|
[] |
[] |
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
|
[
"Jintang Li",
"Huizhe Zhang",
"Ruofan Wu",
"Zulun Zhu",
"Baokun Wang",
"Changhua Meng",
"Zibin Zheng",
"Liang Chen"
] | 18,850 |
https://openreview.net/forum?id=LnLySuf1vp
|
||
[] |
Poster
|
[] |
Automatic Sign Language Translation requires the integration of both computer vision and natural language processing to effectively bridge the communication gap between sign and spoken languages. However, the deficiency in large-scale training data to support sign language translation means we need to leverage resources from spoken language. We introduce, Sign2GPT, a novel framework for sign language translation that utilizes large-scale pretrained vision and language models via lightweight adapters for gloss-free sign language translation. The lightweight adapters are crucial for sign language translation, due to the constraints imposed by limited dataset sizes and the computational requirements when training with long sign videos.We also propose a novel pretraining strategy that directs our encoder to learn sign representations from automatically extracted pseudo-glosses without requiring gloss order information or annotations.We evaluate our approach on two public benchmark sign language translation datasets, namely RWTH-PHOENIX-Weather 2014T and CSL-Daily, and improve on state-of-the-art gloss-free translation performance with a significant margin.
|
[] |
[] |
Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation
|
[
"Ryan Wong",
"Necati Cihan Camgoz",
"Richard Bowden"
] | 18,847 |
https://openreview.net/forum?id=LqaEEs3UxU
|
||
[] |
Poster
|
[] |
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM). Leveraging advances in differentiable, nonconvex characterizations of acyclicity, recent efforts have advocated a continuous constrained optimization paradigm to efficiently explore the space of DAGs. Most existing methods employ lasso-type score functions to guide this search, which (i) require expensive penalty parameter retuning when the \emph{unknown} SEM noise variances change across problem instances; and (ii) implicitly rely on limiting homoscedasticity assumptions. In this work, we propose a new convex score function for sparsity-aware learning of linear DAGs, which incorporates concomitant estimation of scale and thus effectively decouples the sparsity parameter from noise levels. Regularization via a smooth, nonconvex acyclicity penalty term yields CoLiDE ($\textbf{Co}$ncomitant $\textbf{Li}$near $\textbf{D}$AG $\textbf{E}$stimation), a regression-based criterion amenable to efficient gradient computation and closed-form estimation of exogenous noise levels in heteroscedastic scenarios. Our algorithm outperforms state-of-the-art methods without incurring added complexity, especially when the DAGs are larger and the noise level profile is heterogeneous. We also find CoLiDE exhibits enhanced stability manifested via reduced standard deviations in several domain-specific metrics, underscoring the robustness of our novel linear DAG estimator.
|
[] |
[] |
CoLiDE: Concomitant Linear DAG Estimation
|
[
"Seyed Saman Saboksayr",
"Gonzalo Mateos",
"Mariano Tepper"
] |
2310.02895
| 18,192 |
https://openreview.net/forum?id=fGAIgO75dG
|
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