ArticleStatistics & probability letters2022
Learning Sparse Deep Neural Networks with a Spike-and-Slab Prior.
Article in Statistics & probability letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.Briefings in bioinformatics · 2025Article
- A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.Statistica Sinica · 2025Article
- Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior.Journal of data science : JDS · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Deep learning has achieved great successes in many machine learning tasks. However, the deep neural networks (DNNs) are often severely over-parameterized, making them computationally expensive, memory intensive, less interpretable and mis-calibrated. We study sparse DNNs under the Bayesian framework: we establish posterior consistency and structure selection consistency for Bayesian DNNs with a spike-and-slab prior, and illustrate their performance using examples on high-dimensional nonlinear variable selection, large network compression and model calibration. Our numerical results indicate that sparsity is essential for improving the prediction accuracy and calibration of the DNN.
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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.