123 citations · 163 across the 7 of their papers we have counts for
9 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…
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…
Global Convergence of Gradient Descent for Deep Linear Residual Networks
Lei Wu, Qingcan Wang, Chao Ma
We analyze the global convergence of gradient descent for deep linear residual networks by proposing a new initialization: zero-asymmetric (ZAS) initialization. It is motivated by…
The Barron Space and the Flow-induced Function Spaces for Neural Network Models
Weinan E, Chao Ma, Lei Wu
One of the key issues in the analysis of machine learning models is to identify the appropriate function space and norm for the model. This is the set of functions endowed with a q…