61 citations · 285 across the 30 of their papers we have counts for
6 papers · 1 filter
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang +1
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed metho…
Gaussian Process Assisted Active Learning of Physical Laws
Jiuhai Chen, Lulu Kang, Guang Lin
In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in…
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…
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
Sheng Zhang, Guang Lin
Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) t…
Bayesian inverse regression for dimension reduction with small datasets
Xin Cai, Guang Lin, Jinglai Li
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $…
On the anticipative nonlinear filtering problem and its stability
Guang Lin, Yanghui Liu, Samy Tindel
In this paper, we consider an anticipative nonlinear filtering problem, in which the observation noise is correlated with the past of the signal. This new signal-observation model…