34 citations · 83 across the 16 of their papers we have counts for
11 papers · 1 filter
MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
Da Zhang, Bingyu Li, Zhiyuan Zhao +3
Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointl…
GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
Yanchen Xu, Ziheng Jiao, Hongyuan Zhang +1
The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical application…
Learn Beneficial Noise as Graph Augmentation
Siqi Huang, Yanchen Xu, Hongyuan Zhang +1
Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply h…
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
Yanchen Xu, Siqi Huang, Hongyuan Zhang +1
Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable g…
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
Hongyuan Zhang, Yanchen Xu, Sida Huang +1
Inspired by the idea of Positive-incentive Noise (Pi-Noise or -Noise) that aims at learning the reliable noise beneficial to tasks, we scientifically investigate the connection…
Discretize Relaxed Solution of Spectral Clustering via a Non-Heuristic Algorithm
Hongyuan Zhang, Xuelong Li
Spectral clustering and its extensions usually consist of two steps: (1) constructing a graph and computing the relaxed solution; (2) discretizing relaxed solutions. Although the f…
Variational Positive-incentive Noise: How Noise Benefits Models
Hongyuan Zhang, Sida Huang, Yubin Guo +1
A large number of works aim to alleviate the impact of noise due to an underlying conventional assumption of the negative role of noise. However, some existing works show that the…
Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
Hongyuan Zhang, Yanan Zhu, Xuelong Li
Graph neural networks (GNN) suffer from severe inefficiency. It is mainly caused by the exponential growth of node dependency with the increase of layers. It extremely limits the a…
Deep Manifold Learning with Graph Mining
Xuelong Li, Ziheng Jiao, Hongyuan Zhang +1
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision…
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
Xuelong Li, Hongyuan Zhang, Rui Zhang
The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge…
AnchorGAE: General Data Clustering via Bipartite Graph Convolution
Hongyuan Zhang, Jiankun Shi, Rui Zhang +1
Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neu…