82 citations · 210 across the 13 of their papers we have counts for
13 papers
Neuro-symbolic Learning Yielding Logical Constraints
Zenan Li, Yunpeng Huang, Zhaoyu Li +5
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This…
Learning with Logical Constraints but without Shortcut Satisfaction
Zenan Li, Zehua Liu, Yuan Yao +4
Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. How…
Softened Symbol Grounding for Neuro-symbolic Systems
Zenan Li, Yuan Yao, Taolue Chen +4
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 funda…
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Yunpeng Huang, Jingwei Xu, Junyu Lai +9
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 achiev…
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Zenan Li, Fan Nie, Qiao Sun +2
Offline Reinforcement Learning (RL) enables policy learning without active interactions, making it especially appealing for self-driving tasks. Recent successes of Transformers ins…
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Zenan Li, Fan Nie, Qiao Sun +2
Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement lea…
Convergence Analysis of Randomized SGDA under NC-PL Condition for Stochastic Minimax Optimization Problems
Zehua Liu, Zenan Li, Xiaoming Yuan +1
We introduce a new analytic framework to analyze the convergence of the Randomized Stochastic Gradient Descent Ascent (RSGDA) algorithm for stochastic minimax optimization problems…
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Qitian Wu, Wentao Zhao, Zenan Li +2
Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing,…
On Collective Robustness of Bagging Against Data Poisoning
Ruoxin Chen, Zenan Li, Jie Li +2
Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-…
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Han Lu, Zenan Li, Runzhong Wang +3
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research. Despite the…
Joint Distribution across Representation Space for Out-of-Distribution Detection
JingWei Xu, Siyuan Zhu, Zenan Li +1
Deep neural networks (DNNs) have become a key part of many modern software applications. After training and validating, the DNN is deployed as an irrevocable component and applied…
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Zenan Li, Xiaoxing Ma, Chang Xu +3
Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field. A particularly damaging problem is that DNN mode…
Boosting Operational DNN Testing Efficiency through Conditioning
Zenan Li, Xiaoxing Ma, Chang Xu +3
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 mode…