699 citations · 3.7k across the 65 of their papers we have counts for
4 papers · 2 filters
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
Chunyuan Li, Changyou Chen, David Carlson +1
Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods addre…
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Chunyuan Li, Changyou Chen, Kai Fan +1
Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable…
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Changyou Chen, David Carlson, Zhe Gan +2
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayesian analogs to popular stochastic optimization methods; however, this connection is not well studied. We exp…
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Zhe Gan, Chunyuan Li, Ricardo Henao +2
Deep dynamic generative models are developed to learn sequential dependencies in time-series data. The multi-layered model is designed by constructing a hierarchy of temporal sigmo…