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41st International Conference on Machine Learning, ICML 2024

作者： Dong, Huanshuo Wang, Hong Liu, Haoyang Luo, Jian Wang, Jie CAS Key Laboratory of Technology in GIPAS MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

Recent advancements in data-driven approaches, such as Neural Operator (NO), have demonstrated their effectiveness in reducing the solving time of Partial Differential Equations (PDEs). However, one major challenge faced by these approaches is the requirement for a large amount of high-precision training data, which needs significant computational costs during the generation process. To address this challenge, we propose a novel PDE dataset generation algorithm, namely Differential Operator Action in Solution space (DiffOAS), which speeds up the data generation process and enhances the precision of the generated data simultaneously. Specifically, DiffOAS obtains a few basic PDE solutions and then combines them to get solutions. It applies differential operators on these solutions, a process we call'operator action', to efficiently generate precise PDE data points. Theoretical analysis shows that the time complexity of DiffOAS method is one order lower than the existing generation method. Experimental results show that DiffOAS accelerates the generation of large-scale datasets with 10,000 instances by 300 times. Even with just 5% of the generation time, NO trained on the data generated by DiffOAS exhibits comparable performance to that using the existing generation method, which highlights the efficiency of DiffOAS. Copyright 2024 by the author(s)

关键词： Mathematical operators

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Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models 41

Coarse-to-Fine Highlighting: Reducing Knowledge Hallucinatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Qitan Wang, Jie Chen, Hanzhu Li, Bin Zhang, Yongdong Wu, Feng CAS Key Laboratory of Technology in GIPAS MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

Generation of plausible but incorrect factual information, often termed hallucination, has attracted significant research interest. Retrieval-augmented language model (RALM)-which enhances models with up-to-date knowledge-emerges as a promising method to reduce hallucination. However, existing RALMs may instead exacerbate hallucination when retrieving lengthy contexts. To address this challenge, we propose COFT, a novel COarse-to-Fine highlighTing method to focus on different granularity-level key texts, thereby avoiding getting lost in lengthy contexts. Specifically, COFT consists of three components: recaller, scorer, and selector. First, recaller applies a knowledge graph to extract potential key entities in a given context. Second, scorer measures the importance of each entity by calculating its contextual weight. Finally, selector selects high contextual weight entities with a dynamic threshold algorithm and highlights the corresponding paragraphs, sentences, or words in a coarse-to-fine manner. Extensive experiments on the knowledge hallucination benchmark demonstrate the effectiveness of COFT, leading to a superior performance over 30% in the F1 score metric. Moreover, COFT also exhibits remarkable versatility across various long-form tasks, such as reading comprehension and question answering. Copyright 2024 by the author(s)

关键词： Knowledge graph

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IMAGE2SENTENCE BASED ASYMMETRIC ZERO-SHOT COMPOSED IMAGE RETRIEVAL 12

IMAGE2SENTENCE BASED ASYMMETRIC ZERO-SHOT COMPOSED IMAGE RET...

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12th International Conference on Learning Representations, ICLR 2024

作者： Du, Yongchao Wang, Min Zhou, Wengang Hui, Shuping Li, Houqiang CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments when deploying the large vision-language model. To tackle the above problems, we propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning. In the framework, we propose a new adaptive token learner that maps an image to a sentence in the word embedding space of VL model. The sentence adaptively captures discriminative visual information and is further integrated with the text modifier. An asymmetric structure is devised for flexible deployment, in which the lightweight model is adopted for the query side while the large VL model is deployed on the gallery side. The global contrastive distillation and the local alignment regularization are adopted for the alignment between the light model and the VL model for CIR task. Our experiments demonstrate that the proposed ISA could better cope with the real retrieval scenarios and further improve retrieval accuracy and efficiency. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image retrieval

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ACCELERATING DATA GENERATION FOR NEURAL OPERATORS VIA KRYLOV SUBSPACE RECYCLING 12

ACCELERATING DATA GENERATION FOR NEURAL OPERATORS VIA KRYLOV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hong Hao, Zhongkai Wang, Jie Geng, Zijie Wang, Zhen Li, Bin Wu, Feng CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Tsinghua University China

Learning neural operators for solving partial differential equations (PDEs) has attracted great attention due to its high inference efficiency. However, training such operators requires generating a substantial amount of labeled data, i.e., PDE problems together with their solutions. The data generation process is exceptionally time-consuming, as it involves solving numerous systems of linear equations to obtain numerical solutions to the PDEs. Many existing methods solve these systems independently without considering their inherent similarities, resulting in extremely redundant computations. To tackle this problem, we propose a novel method, namely Sorting Krylov Recycling (SKR), to boost the efficiency of solving these systems, thus significantly accelerating data generation for neural operators training. To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators. The working horse of SKR is Krylov subspace recycling, a powerful technique for solving a series of interrelated systems by leveraging their inherent similarities. Specifically, SKR employs a sorting algorithm to arrange these systems in a sequence, where adjacent systems exhibit high similarities. Then it equips a solver with Krylov subspace recycling to solve the systems sequentially instead of independently, thus effectively enhancing the solving efficiency. Both theoretical analysis and extensive experiments demonstrate that SKR can significantly accelerate neural operator data generation, achieving a remarkable speedup of up to 13.9 times. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Sorting

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Reinforcement Learning within Tree Search for Fast Macro Placement 41

Reinforcement Learning within Tree Search for Fast Macro Pla...

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41st International Conference on Machine Learning, ICML 2024

作者： Geng, Zijie Wang, Jie Liu, Ziyan Xu, Siyuan Tang, Zhentao Yuan, Mingxuan Hao, Jianye Zhang, Yongdong Wu, Feng CAS Key Laboratory of Technology in GIPAS MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei China Tianjin University Tianjin China

Macro placement is a crucial step in modern chip design, and reinforcement learning (RL) has recently emerged as a promising technique for improving the placement quality. However, existing RL-based techniques are hindered by their low sample efficiency, requiring numerous online rollouts or substantial offline expert data to achieve bootstrap, which are often impractical in industrial scenarios. To address this challenge, we propose a novel sample-efficient framework, namely EfficientPlace, for fast macro placement. EfficientPlace integrates a global tree search algorithm to strategically direct the optimization process, as well as a RL agent for local policy learning to advance the tree search. Experiments on commonly used benchmarks demonstrate that EfficientPlace achieves remarkable placement quality within a short timeframe, outperforming recent state-of-the-art approaches. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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A GENERAL RANK PRESERVING FRAMEWORK FOR ASYMMETRIC IMAGE RETRIEVAL 11

A GENERAL RANK PRESERVING FRAMEWORK FOR ASYMMETRIC IMAGE RET...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wu, Hui Wang, Min Zhou, Wengang Li, Houqiang CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Asymmetric image retrieval aims to deploy compatible models on platforms of different resources to achieve a balance between computational efficiency and retrieval accuracy. The most critical issue is how to align the output features of different models. Despite the great progress, existing approaches apply strong constraints so that features or neighbor structures are strictly aligned across different models. However, such a one-to-one constraint is too strict to be well preserved for the query models with low capacity. Considering that the primary concern of the users is the rank of the returned images, we propose a generic rank preserving framework, which achieves feature compatibility and the order consistency between query and gallery models simultaneously. Specifically, we propose two alternatives to instantiate the framework. One realizes straightforward rank order preservation by directly preserving the consistency of the sorting results. To make sorting process differentiable, the Heaviside step function in sorting is approximated by the sigmoid function. The other aims to preserve a learnable monotonic mapping relationship between the returned similarity scores of query and gallery models. The mapped similarity scores of gallery model are considered as pseudo-supervision to guide the query model training. Extensive experiments on various large-scale datasets demonstrate the superiority of our two proposed methods. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Image retrieval

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RETHINKING BRANCHING ON EXACT COMBINATORIAL OPTIMIZATION SOLVER: THE FIRST DEEP SYMBOLIC DISCOVERY FRAMEWORK 12

RETHINKING BRANCHING ON EXACT COMBINATORIAL OPTIMIZATION SOL...

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12th International Conference on Learning Representations, ICLR 2024

作者： Kuang, Yufei Wang, Jie Liu, Haoyang Zhu, Fangzhou Li, Xijun Zeng, Jia Hao, Jianye Li, Bin Wu, Feng CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei Technologies China

Machine learning (ML) has been shown to successfully accelerate solving NP-hard combinatorial optimization (CO) problems under the branch and bound framework. However, the high training and inference cost and limited interpretability of ML approaches severely limit their wide application to modern exact CO solvers. In contrast, human-designed policies-though widely integrated in modern CO solvers due to their compactness and reliability-can not capture data-driven patterns for higher performance. To combine the advantages of the two paradigms, we propose the first symbolic discovery framework-namely, deep symbolic discovery for exact combinatorial optimization solver (Symb4CO)-to learn high-performance symbolic policies on the branching task. Specifically, we show the potential existence of small symbolic policies empirically, employ a large neural network to search in the high-dimensional discrete space, and compile the learned symbolic policies directly for fast deployment. Experiments show that the Symb4CO learned purely CPU-based policies consistently achieve comparable performance to previous GPU-based state-of-the-art approaches. Furthermore, the appealing features of Symb4CO include its high training (ten training instances) and inference (one CPU core) efficiency and good interpretability (one-line expressions), making it simple and reliable for deployment. The results show encouraging potential for the wide deployment of ML to modern solvers. Codes are available at https://***/MIRALab-USTC/L2O-Symb4CO. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Combinatorial optimization

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A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design 41

A Circuit Domain Generalization Framework for Efficient Logi...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Zhihai Chen, Lei Wang, Jie Bai, Yinqi Li, Xing Li, Xijun Yuan, Mingxuan Hao, Jianye Zhang, Yongdong Wu, Feng CAS Key Laboratory of Technology in GIPAS & MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei Technologies China College of Intelligence and Computing Tianjin University China

Logic Synthesis (LS) plays a vital role in chip design. A key task in LS is to simplify circuits-modeled by directed acyclic graphs (DAGs)-with functionality-equivalent transformations. To tackle this task, many LS heuristics apply transformations to subgraphs-rooted at each node on an input DAG-sequentially. However, we found that a large number of transformations are ineffective, which makes applying these heuristics highly time-consuming. In particular, we notice that the runtime of the Resub and Mfs2 heuristics often dominates the overall runtime of LS optimization processes. To address this challenge, we propose a novel data-driven LS heuristic paradigm, namely PruneX, to reduce ineffective transformations. The major challenge of developing PruneX is to learn models that well generalize to unseen circuits, i.e., the out-of-distribution (OOD) generalization problem. Thus, the major technical contribution of PruneX is the novel circuit domain generalization framework, which learns domain-invariant representations based on the transformation-invariant domain-knowledge. To the best of our knowledge, PruneX is the first approach to tackle the OOD problem in LS heuristics. We integrate PruneX with the aforementioned Resub and Mfs2 heuristics. Experiments demonstrate that PruneX significantly improves their efficiency while keeping comparable optimization performance on industrial and very large-scale circuits, achieving up to 3.1× faster runtime. Copyright 2024 by the author(s)

关键词： Logic Synthesis

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Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph 41

Towards General Algorithm Discovery for Combinatorial Optimi...

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41st International Conference on Machine Learning, ICML 2024

作者： Kuang, Yufei Wang, Jie Zhou, Yuyan Li, Xijun Zhu, Fangzhou Hao, Jianye Wu, Feng CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Noah's Ark Lab Huawei Technologies China College of Intelligence and Computing Tianjin University China

Machine learning (ML) approaches have been successfully applied to accelerating exact combinatorial optimization (CO) solvers. However, many of them fail to explain what patterns they have learned that accelerate the CO algorithms due to the black-box nature of ML models like neural networks, and thus they prevent researchers from further understanding the tasks they are interested in. To tackle this problem, we propose the first graph-based algorithm discovery framework-namely, graph symbolic discovery for exact combinatorial optimization solver (GS4CO)-that learns interpretable branching policies directly from the general bipartite graph representation of CO problems. Specifically, we mainly focus on the variable selection part of the branching policy. We design a unified representation for symbolic variable selection policies with graph inputs, and then we employ a Transformer with multiple tree-structural encodings to generate symbolic trees end-to-end, which effectively reduces the cumulative error from iteratively distilling graph neural networks. Experiments show that GS4CO learned interpretable and lightweight policies outperform all the baselines on CPU machines, including both the human-designed and the learning-based. GS4CO shows an encouraging step towards general algorithm discovery on modern CO solvers. Codes are available at https://***/MIRALab-USTC/L2O-GS4CO. Copyright 2024 by the author(s)

关键词： Combinatorial optimization

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MOTIONRL: ALIGN TEXT-TO-MOTION GENERATION TO HUMAN PREFERENCES WITH MULTI-REWARD REINFORCEMENT LEARNING

arXiv

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arXiv 2024年

作者： Liu, Xiaoyang Mao, Yunyao Zhou, Wengang Li, Houqiang CAS Key Laboratory of Technology in GIPAS EEIS Department University of Science and Technology of China China

We introduce MotionRL, the first approach to utilize Multi-Reward Reinforcement Learning (RL) for optimizing text-to-motion generation tasks and aligning them with human preferences. Previous works focused on improving numerical performance metrics on the given datasets, often neglecting the variability and subjectivity of human feedback. In contrast, our novel approach uses reinforcement learning to fine-tune the motion generator based on human preferences prior knowledge of the human perception model, allowing it to generate motions that better align human preferences. In addition, MotionRL introduces a novel multi-objective optimization strategy to approximate Pareto optimality between text adherence, motion quality, and human preferences. Extensive experiments and user studies demonstrate that MotionRL not only allows control over the generated results across different objectives but also significantly enhances performance across these metrics compared to other algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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