61 citations · 285 across the 30 of their papers we have counts for
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stat.ML2019
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…
stat.ML2019★ 8 cited
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…