1 citations · 1 across the 4 of their papers we have counts for
4 papers
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen +4
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advan…
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
Zirui Liu, Shengyuan Chen, Kaixiong Zhou +3
The training of graph neural networks (GNNs) is extremely time consuming because sparse graph-based operations are hard to be accelerated by hardware. Prior art explores trading of…
DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators
Sheng Chen, Xu Wang, Chao Chen +3
Modern neural network modules which can significantly enhance the learning power usually add too much computational complexity to the original neural networks. In this paper, we pu…
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
Sheng-Wei Chen, Chun-Nan Chou, Edward Y. Chang
For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate…