Search Results for author: Zenan Li

Found 11 papers, 6 papers with code

A Survey on Deep Learning for Theorem Proving

1 code implementation15 Apr 2024 Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, Xujie Si

Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.

Automated Theorem Proving

Learning with Logical Constraints but without Shortcut Satisfaction

1 code implementation1 Mar 2024 Zenan Li, Zehua Liu, Yuan YAO, Jingwei Xu, Taolue Chen, Xiaoxing Ma, Jian Lü

In this paper, we present a new framework for learning with logical constraints.

Softened Symbol Grounding for Neuro-symbolic Systems

1 code implementation1 Mar 2024 Zenan Li, Yuan YAO, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma, Jian Lü

Neuro-symbolic learning generally consists of two separated worlds, i. e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI.

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

1 code implementation21 Nov 2023 Yunpeng Huang, Jingwei Xu, Junyu Lai, Zixu Jiang, Taolue Chen, Zenan Li, Yuan YAO, Xiaoxing Ma, Lijuan Yang, Hao Chen, Shupeng Li, Penghao Zhao

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).


Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

no code implementations28 Sep 2023 Zenan Li, Fan Nie, Qiao Sun, Fang Da, Hang Zhao

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks.

Autonomous Driving Offline RL +1

Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills

no code implementations24 Sep 2023 Zenan Li, Fan Nie, Qiao Sun, Fang Da, Hang Zhao

Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets.

Autonomous Driving Offline RL +2

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

1 code implementation14 Jun 2023 Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, Junchi Yan

In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \textsc{NodeFormer}.

Graph structure learning Image Classification

A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs

no code implementations28 Dec 2021 Han Lu, Zenan Li, Runzhong Wang, Qibing Ren, Junchi Yan, Xiaokang Yang

Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.

Adversarial Attack Combinatorial Optimization

Joint Distribution across Representation Space for Out-of-Distribution Detection

no code implementations23 Mar 2021 Jingwei Xu, Siyuan Zhu, Zenan Li, Chang Xu

Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across representation spaces.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Operational Calibration: Debugging Confidence Errors for DNNs in the Field

no code implementations6 Oct 2019 Zenan Li, Xiaoxing Ma, Chang Xu, Jingwei Xu, Chun Cao, Jian Lü

Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field.

Boosting Operational DNN Testing Efficiency through Conditioning

1 code implementation6 Jun 2019 Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao, Jingwei Xu, Jian Lü

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions.

DNN Testing

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