1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
Huifa Li, Jie Fu, Xinlin Zhuang +6
Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due…
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Tong Cheng, Jie Fu, Xinpeng Ling +4
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from var…
CBNN: 3-Party Secure Framework for Customized Binary Neural Networks Inference
Benchang Dong, Zhili Chen, Xin Chen +3
Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with…
Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
Huifa Li, Jie Fu, Xinpeng Ling +3
The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contrib…
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Huifa Li, Jie Fu, Zhili Chen +3
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-c…