activity
20232025
most citedAsymmetrically Decentralized Federated Learning

2 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

q-bio.QM2025

Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution

Jiaqi Xiong, Nan Yin, Shiyang Liang +5

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced.…

cs.CV2024

AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation

Houcheng Su, Mengzhu Wang, Jiao Li +3

In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its…

cs.CV20241 cited

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Mengzhu Wang, Jiao Li, Houcheng Su +3

Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing da…

cs.CV20241 cited

DiM: -Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation

Bingli Wang, Houcheng Su, Nan Yin +2

As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation…

cs.LG2024

Pre-insertion resistors temperature prediction based on improved WOA-SVR

Honghe Dai, Site Mo, Haoxin Wang +3

The pre-insertion resistors (PIR) within high-voltage circuit breakers are critical components and warm up by generating Joule heat when an electric current flows through them. Ele…

cs.LG20232 cited

Asymmetrically Decentralized Federated Learning

Qinglun Li, Miao Zhang, Nan Yin +2

To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, whic…