18 citations · 32 across the 21 of their papers we have counts for
21 papers
Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective
Peiyao Wang, Liang Bai, Xian Yang +2
Graph neural networks (GNNs) have emerged as a fundamental tool for learning from graph-structured data, achieving strong performance across a wide range of applications. However,…
Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4
Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a continuous-time modeling method…
Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment
Lijun Zhang, Lin Li, Wei Wei +5
When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existi…
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Xiaoqi Han, Víctor Gutiérrez-Basulto, Ru Li +3
Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all kn…
Learnable Game-theoretic Policy Optimization for Data-centric Self-explanation Rationalization
Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu +3
Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input…
Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective
Yunxiao Zhao, Hao Xu, Zhiqiang Wang +3
Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by th…
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
Yi Ma, Hongyao Tang, Chenjun Xiao +4
In recent years, the expansion of neural network models and training data has driven remarkable progress in deep learning, particularly in computer vision and natural language proc…
Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +6
Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs). Retrieved knowledge can be…
Positive region preserved random sampling: an efficient feature selection method for massive data
Hexiang Bai, Deyu Li, Jiye Liang +1
Selecting relevant features is an important and necessary step for intelligent machines to maximize their chances of success. However, intelligent machines generally have no enough…
A Survey of Continual Reinforcement Learning
Chaofan Pan, Xin Yang, Yanhua Li +4
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field…
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Xin Zhang, Liang Bai, Xian Yang +1
Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks evolve over time. Low-Rank Adapt…
Progressive Local Alignment for Medical Multimodal Pre-training
Huimin Yan, Xian Yang, Liang Bai +1
Local alignment between medical images and text is essential for accurate diagnosis, though it remains challenging due to the absence of natural local pairings and the limitations…
Towards Effective Graph Rationalization via Boosting Environment Diversity
Yujie Wang, Kui Yu, Yuhong Zhang +2
Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize well in the face of distribution…
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning
Jianqing Liang, Xinkai Wei, Min Chen +2
Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of l…
Atomic Fact Decomposition Helps Attributed Question Answering
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +3
Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, includ…
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
Lei Guan, Dongsheng Li, Yongle Chen +3
Asynchronous pipeline model parallelism with a "1F1B" (one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, th…
Towards Privacy-Aware Causal Structure Learning in Federated Setting
Jianli Huang, Xianjie Guo, Kui Yu +2
Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure lear…
Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection
Zhangjian Ji, Kai Feng, Yuhua Qian +1
Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is…
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Ming Li, Sho Sonoda, Feilong Cao +2
We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignm…
Logic could be learned from images
Qian Guo, Yuhua Qian, Xinyan Liang +3
Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to des…
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams
Junhong Wang, Shuliang Xu, Bingqian Duan +2
Data stream mining problem has caused widely concerns in the area of machine learning and data mining. In some recent studies, ensemble classification has been widely used in conce…