61 citations · 285 across the 30 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…
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…
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…
Turbulence Generation from a stochastic wavelet model
Yifan Du, Guang Lin
This research presents a new turbulence generation method based on stochastic wavelets and tests its various properties in both homogeneous and inhomogeneous turbulence. Turbulence…
A two-level stochastic collocation method for semilinear elliptic equations with random coefficients
Luoping Chen, Bin Zheng, Guang Lin +1
In this work, we propose a novel two-level discretization for solving semilinear elliptic equations with random coefficients. Motivated by the two-grid method for deterministic par…