82 citations · 128 across the 8 of their papers we have counts for
8 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…
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
Yunpeng Huang, Yaonan Gu, Jingwei Xu +3
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, dispari…
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…
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…