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math.NA2020
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…
math.NA2020
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…
math.NA2019
Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
Yating Wang, Guang Lin
We present efficient deep learning techniques for approximating flow and transport equations for both single phase and two-phase flow problems. The proposed methods take advantages…