6 citations · 17 across the 8 of their papers we have counts for
5 papers · 1 filter
An adaptive Hessian approximated stochastic gradient MCMC method
Yating Wang, Wei Deng, Guang Lin
Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), whic…
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Wei Deng, Qi Feng, Georgios Karagiannis +2
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction f…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao +2
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC…
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
Yating Wang, Wei Deng, Lin Guang
In this work, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hi…
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
Wei Deng, Junwei Pan, Tian Zhou +3
Click-through rate (CTR) prediction is a crucial task in online display advertising. The embedding-based neural networks have been proposed to learn both explicit feature interacti…