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Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

arXiv 1806.05975 ↗First posted 13 Jun 2018stat.ML cs.LGcited by 35

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Abstract (arXiv)

Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selection---even choosing the number of nodes---remains an open question. Recent work has proposed the use of a horseshoe prior over node pre-activations of a Bayesian neural network, which effectively turns off nodes that do not help explain the data. In this work, we propose several modeling and inference advances that consistently improve the compactness of the model learned while maintaining predictive performance, especially in smaller-sample settings including reinforcement learning.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2018.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 7; 2024: 7; 2025: 3; 2026: 1.

35 papers.

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  8. 2022Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsitycited by 3
  9. 2023Streaming LifeLong Learning With Any-Time Inferencecited by 3
  10. 2025Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Modelscited by 3
  11. 2021Revisiting Self-Supervised Monocular Depth Estimationcited by 2
  12. 2023Are Visual Recognition Models Robust to Image Compression?cited by 2
  13. 2024Model and Feature Diversity for Bayesian Neural Networks in Mutual Learningcited by 2
  14. 2022Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approachcited by 1
  15. 2023Bayesian sparsification for deep neural networks with Bayesian model reductioncited by 1
  16. 2024DAGnosis: Localized Identification of Data Inconsistencies using Structurescited by 1
  17. 2025Vector Copula Variational Inference and Dependent Block Posterior Approximationscited by 1
  18. 2021Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networkcited by 0
  19. 2022Interpretable Latent Variables in Deep State Space Modelscited by 0
  20. 2023Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networkscited by 0
  21. 2024Variational Bayesian Bow tie Neural Networks with Shrinkagecited by 0
  22. 2025Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modelingcited by 0
  23. 2026Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimationcited by 0