29 citations · 49 across the 6 of their papers we have counts for
4 papers · 1 filter
On Computation and Generalization of GANs with Spectrum Control
Haoming Jiang, Zhehui Chen, Minshuo Chen +3
Generative Adversarial Networks (GANs), though powerful, is hard to train. Several recent works (brock2016neural,miyato2018spectral) suggest that controlling the spectra of weight…
Learning to Defend by Learning to Attack
Haoming Jiang, Zhehui Chen, Yuyang Shi +2
Adversarial training provides a principled approach for training robust neural networks. From an optimization perspective, adversarial training is essentially solving a bilevel opt…
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
Zhehui Chen, Xingguo Li, Lin F. Yang +2
We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a mini…
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Tianyi Liu, Zhehui Chen, Enlu Zhou +1
Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning, e.g., training deep neural networks, vari…