277 citations · 811 across the 38 of their papers we have counts for
5 papers · 1 filter
Modularizing while Training: A New Paradigm for Modularizing DNN Models
Binhang Qi, Hailong Sun, Hongyu Zhang +2
Deep neural network (DNN) models have become increasingly crucial components in intelligent software systems. However, training a DNN model is typically expensive in terms of both…
Learning Regularized Positional Encoding for Molecular Prediction
Xiang Gao, Weihao Gao, Wenzhi Xiao +3
Machine learning has become a promising approach for molecular modeling. Positional quantities, such as interatomic distances and bond angles, play a crucial role in molecule physi…
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
Jiaxiang Tang, Wei Hu, Xiang Gao +1
Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an a…
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
Xiang Gao, Wei Hu, Zongming Guo
Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs a…
Deep reinforcement learning for time series: playing idealized trading games
Xiang Gao
Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading gam…