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A curated list of resources dedicated to bayesian deep learning
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| 2026-07-31 | 416 |
| 2026-08-03 | 416 |
| 2026-08-06 | 416 |
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# awesome-bayesian-deep-learning
A curated list of resources dedicated to bayesian deep learning
## Table of Contents
- [Theory](#theory)
- [Papers / Thesis](#papers--thesis)
## Theory
### Papers / Thesis
#### 2013:
1. Deep gaussian processes|Andreas C. Damianou,Neil D. Lawrence|2013 <br>
Source: http://www.jmlr.org/proceedings/papers/v31/damianou13a.pdf
#### 2014:
1. Avoiding pathologies in very deep networks|D Duvenaud, O Rippel, R Adams|2014 <br>
Source: http://www.jmlr.org/proceedings/papers/v33/duvenaud14.pdf
2. Nested variational compression in deep Gaussian processes|J Hensman, ND Lawrence|2014
Source: https://arxiv.org/abs/1412.1370
#### 2015:
1. On Modern Deep Learning and Variational Inference |Yarin Gal, Zoubin Ghahramani|2015 <br>
Source: http://www.approximateinference.org/accepted/GalGhahramani2015.pdf
2. Rapid Prototyping of Probabilistic Models: Emerging Challenges in Variational Inference |Yarin Gal, |2015<br>
Source: http://www.approximateinference.org/accepted/Gal2015.pdf
3. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference |Yarin Gal, Zoubin Ghahramani|2015<br>
Source: http://arxiv.org/abs/1506.02158
4. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning |Yarin Gal, Zoubin Ghahramani|2015<br>
Source: http://arxiv.org/abs/1506.02142
5. Dropout as a Bayesian Approximation: Insights and Applications |Yarin Gal, |2015
Source: https://sites.google.com/site/deeplearning2015/33.pdf?attredirects=0
6. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference |Yarin Gal, Zoubin Ghahramani|2015<br>
Source: http://arxiv.org/abs/1506.02158
7. Scalable Variational Gaussian Process Classification|J Hensman, AGG Matthews, Z Ghahramani|2015
Source: http://www.jmlr.org/proceedings/papers/v38/hensman15.pdf
#### 2016:
1. Relativistic Monte Carlo | Xiaoyu Lu| 2016 <br>
Source: https://arxiv.org/abs/1609.04388
2. Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout | Ian Osband| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_4.pdf
3. Semi-supervised deep kernel learning|Neal Jean, Michael Xie, Stefano Ermon|2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_5.pdf
4. Categorical Reparameterization with Gumbel-Softmax| Eric Jang, Shixiang Gu,Ben Poole| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_8.pdf
Video: https://www.youtube.com/watch?v=JFgXEbgcT7g
5. Learning to Optimise: Using Bayesian Deep Learning for Transfer Learning in Optimisation| Jonas Langhabel, Jannik Wolff| 2016<br>
Source: http://bayesiandeeplearning.org/papers/BDL_9.pdf
6. One-Shot Learning in Discriminative Neural Networks| Jordan Burgess,James Robert Lloyd,Zoubin Ghahramani| 2016<br>
Source: http://bayesiandeeplearning.org/papers/BDL_10.pdf
7. Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation| Leonard Hasenclever,
Stefan Webb| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_11.pdf
8. Knots in random neural networks| Kevin K. Chen| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_2.pdf
9. Discriminative Bayesian neural networks know what they do not know | Christian Leibig, Siegfried Wahl| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_12.pdf
10. Variational Inference in Neural Networks using an Approximate Closed-Form Objective|Wolfgang Roth and Franz Pernkopf|2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_13.pdf
11. Combining sequential deep learning and variational Bayes for semi-supervised inference| Jos van der Westhuizen, Dr. Joan Lasenby| 2016 <br>
Source: http://bayesiandeeplearning.org/papers/BDL_14.pdf
12. Importance Weighted Autoencoders with Random Neural Network Parameters| Daniel Hernández-Lobato,Thang D. Bui,Yinzhen Li| 2016
Stefan Webb| 2016 <br>
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