2 citations · 7 across the 5 of their papers we have counts for
6 papers
Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement
Kai Gao, Runzhi He, Bing Xie +1
Deep learning (DL) package supply chains (SCs) are critical for DL frameworks to remain competitive. However, vital knowledge on the nature of DL package SCs is still lacking. In t…
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Jinsong Chen, Chang Liu, Kaiyuan Gao +2
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To thi…
Deep Contrastive One-Class Time Series Anomaly Detection
Rui Wang, Chongwei Liu, Xudong Mou +5
The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based metho…
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…
EAdam Optimizer: How Impact Adam
Wei Yuan, Kai-Xin Gao
Many adaptive optimization methods have been proposed and used in deep learning, in which Adam is regarded as the default algorithm and widely used in many deep learning frameworks…