Bayesian Deep Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Core papers on Bayesian deep learning, uncertainty estimation, and scalable inference for neural networks.

Bayesian Deep Learning: 10 key papers

1. Weight Uncertainty in Neural Networks
   Blundell et al. arXiv 2015.
2. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
   Gal and Ghahramani. arXiv 2015.
3. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
   Kendall and Gal. arXiv 2017.
4. A Practical Bayesian Framework for Backpropagation Networks
   MacKay. Neural Computation 1992.
5. Bayesian Deep Learning and a Probabilistic Perspective of Generalization
   Wilson and Izmailov. arXiv 2020.
6. How Good is the Bayes Posterior in Deep Neural Networks Really?
   Wenzel et al. arXiv 2020.
7. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
   Hernández-Lobato and Adams. arXiv 2015.
8. On the Importance of Strong Baselines in Bayesian Deep Learning
   Mukhoti et al. arXiv 2018.
9. Deep Ensembles: A Loss Landscape Perspective
   Fort et al. arXiv 2019.
10. A Simple Baseline for Bayesian Uncertainty in Deep Learning
    Maddox et al. arXiv 2019.

Related reading lists

• Bayesian RL & Thompson Sampling
• Uncertainty Quantification & Conformal Prediction
• Bayesian Optimization & Global Optimization
• Active Learning & Adaptive Sampling
• Optimization for Deep Learning
• Generative Modeling
• Contrastive & Self-Supervised Learning
• Multi-Armed Bandits & Best-Arm Identification