42 citations · 91 across the 6 of their papers we have counts for
6 papers
An Expectation-Maximization Algorithm for Continuous-time Hidden Markov Models
Qingcan Wang, Weinan E
We propose a unified framework that extends the inference methods for classical hidden Markov models to continuous settings, where both the hidden states and observations occur in…
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…
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections
Weinan E, Chao Ma, Qingcan Wang +1
The behavior of the gradient descent (GD) algorithm is analyzed for a deep neural network model with skip-connections. It is proved that in the over-parametrized regime, for a suit…
A Priori Estimates of the Population Risk for Residual Networks
Weinan E, Chao Ma, Qingcan Wang
Optimal a priori estimates are derived for the population risk, also known as the generalization error, of a regularized residual network model. An important part of the regularize…
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions
Weinan E, Qingcan Wang
We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
Ruying Bao, Sihang Liang, Qingcan Wang
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. I…