20 citations · 30 across the 4 of their papers we have counts for
5 papers
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +5
Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of…
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +6
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR…
Eigenvalue-corrected Natural Gradient Based on a New Approximation
Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang +5
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers. A recently proposed method, Eigenvalue-corrected Kronecker Factoriz…
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang +4
Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-ord…
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Xiaolei Liu, Yuheng Luo, Xiaosong Zhang +1
Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from ad…