ArticleAdvances in neural information processing systems2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning.
Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.Statistica Sinica · 2025Article
- Extended fiducial inference: toward an automated process of statistical inference.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Extended fiducial inference for individual treatment effects via deep neural networks.Statistics and computing · 2025Article
- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Causal-StoNet: Causal Inference for High-Dimensional Complex Data.... International Conference on Learning Representations · 2024Article
- A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Stochastic gradient Langevin dynamics with adaptive drifts.Journal of statistical computation and simulation · 2022Article
- Multi-scale inference of genetic trait architecture using biologically annotated neural networks.PLoS genetics · 2021Article
- A Variational Approximation for Analyzing the Dynamics of Panel Data.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2021Article
- Non-convex Learning via Replica Exchange Stochastic Gradient MCMC.Proceedings of machine learning research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We propose a novel adaptive empirical Bayesian (AEB) method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (MCMC) and smoothly optimizing the hyperparameters using stochastic approximation (SA). We further prove the convergence of the proposed method to the asymptotically correct distribution under mild conditions. Empirical applications of the proposed method lead to the state-of-the-art performance on MNIST and Fashion MNIST with shallow convolutional neural networks (CNN) and the state-of-the-art compression performance on CIFAR10 with Residual Networks. The proposed method also improves resistance to adversarial attacks.
Identifiers
33244209PMC7687285What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.