4 citations · 6 across the 3 of their papers we have counts for
4 papers
Adaptively Solving the Local-Minimum Problem for Deep Neural Networks
Huachuan Wang, James Ting-Ho Lo
This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training D…
Low-Order Model of Biological Neural Networks
Huachuan Wang, James Ting-Ho Lo
A biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/…
Adopting Robustness and Optimality in Fitting and Learning
Zhiguang Wang, Tim Oates, James Lo
We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index to for achieving robustness to outliers by optimizing a quasi-Minimin fun…
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
Zhiguang Wang, Tim Oates, James Lo
This paper proposes a set of new error criteria and learning approaches, Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex optimization problem in traini…