123 citations · 711 across the 24 of their papers we have counts for
6 papers · 1 filter
Complexity Measures for Neural Networks with General Activation Functions Using Path-based Norms
Zhong Li, Chao Ma, Lei Wu
A simple approach is proposed to obtain complexity controls for neural networks with general activation functions. The approach is motivated by approximating the general activation…
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't
Weinan E, Chao Ma, Stephan Wojtowytsch +1
The purpose of this article is to review the achievements made in the last few years towards the understanding of the reasons behind the success and subtleties of neural network-ba…
A Qualitative Study of the Dynamic Behavior for Adaptive Gradient Algorithms
Chao Ma, Lei Wu, Weinan E
The dynamic behavior of RMSprop and Adam algorithms is studied through a combination of careful numerical experiments and theoretical explanations. Three types of qualitative featu…
The Slow Deterioration of the Generalization Error of the Random Feature Model
Chao Ma, Lei Wu, Weinan E
The random feature model exhibits a kind of resonance behavior when the number of parameters is close to the training sample size. This behavior is characterized by the appearance…
The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models
Chao Ma, Lei Wu, Weinan E
A numerical and phenomenological study of the gradient descent (GD) algorithm for training two-layer neural network models is carried out for different parameter regimes when the t…
Machine learning based non-Newtonian fluid model with molecular fidelity
Huan Lei, Lei Wu, Weinan E
We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Dumbbell polymer solutions ar…