14 citations · 28 across the 8 of their papers we have counts for
5 papers · 1 filter
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
Liyue Chen, Linian Wang, Jinyu Xu +5
Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become simi…
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Yuke Hu, Wei Liang, Ruofan Wu +5
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging fe…
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
Ruofan Wu, Jiawei Qiao, Mingzhe Wu +5
We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative…