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A curated list of resources dedicated to bayesian deep learning
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README.md Update README.md May 10, 2017

README.md

awesome-bayesian-deep-learning

A curated list of resources dedicated to bayesian deep learning

Table of Contents

Theory

Papers / Thesis

2013:

  1. Deep gaussian processes|Andreas C. Damianou,Neil D. Lawrence|2013
    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
    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
    Source: http://www.approximateinference.org/accepted/GalGhahramani2015.pdf
  2. Rapid Prototyping of Probabilistic Models: Emerging Challenges in Variational Inference |Yarin Gal, |2015
    Source: http://www.approximateinference.org/accepted/Gal2015.pdf
  3. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference |Yarin Gal, Zoubin Ghahramani|2015
    Source: http://arxiv.org/abs/1506.02158
  4. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning |Yarin Gal, Zoubin Ghahramani|2015
    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
    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
    Source: https://arxiv.org/abs/1609.04388
  2. Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout | Ian Osband| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_4.pdf
  3. Semi-supervised deep kernel learning|Neal Jean, Michael Xie, Stefano Ermon|2016
    Source: http://bayesiandeeplearning.org/papers/BDL_5.pdf
  4. Categorical Reparameterization with Gumbel-Softmax| Eric Jang, Shixiang Gu,Ben Poole| 2016
    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
    Source: http://bayesiandeeplearning.org/papers/BDL_9.pdf
  6. One-Shot Learning in Discriminative Neural Networks| Jordan Burgess,James Robert Lloyd,Zoubin Ghahramani| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_10.pdf
  7. Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation| Leonard Hasenclever, Stefan Webb| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_11.pdf
  8. Knots in random neural networks| Kevin K. Chen| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_2.pdf
  9. Discriminative Bayesian neural networks know what they do not know | Christian Leibig, Siegfried Wahl| 2016
    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
    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
    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
    Source: http://bayesiandeeplearning.org/papers/BDL_15.pdf
  13. Variational Graph Auto-Encoders| Thomas N. Kipf,Max Welling| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_16.pdf
  14. Dropout-based Automatic Relevance Determination| Dmitry Molchanov, Arseniy Ashuha, Dmitry Vetrov| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_18.pdf
  15. Scalable GP-LSTMs with Semi-Stochastic Gradients| Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu and Eric P. Xing| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_19.pdf
  16. Approximate Inference for Deep Latent Gaussian Mixture Models|Eric Nalisnick, Lars Hertel and Padhraic Smyth|2016
    Source: http://bayesiandeeplearning.org/papers/BDL_20.pdf
  17. Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Training | Dilin Wang, Yihao Feng and Qiang Liu| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_21.pdf Video: https://www.youtube.com/watch?v=fi-UUQe2Pss
  18. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks| Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez and Steffen Udluft| 2016
    Source: https://arxiv.org/abs/1605.07127
  19. Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels | Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi and Maurizio Filippone| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_24.pdf
  20. Embedding Words as Distributions with a Bayesian Skip-gram Model | Arthur Bražinskas, Serhii Havrylov and Ivan Titov| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_25.pdf
  21. Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models|Mohammad Emtiyaz Khan and Wu Lin|2016
    Source: http://bayesiandeeplearning.org/papers/BDL_26.pdf
  22. Neural Variational Inference for Latent Dirichlet Allocation| Akash Srivastava and Charles Sutton| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_27.pdf
  23. Hierarchical Bayesian Neural Networks for Personalized Classification | Ajjen Joshi, Soumya Ghosh, Margrit Betke and Hanspeter Pfister| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_28.pdf
  24. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles| Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_29.pdf
  25. Asynchronous Stochastic Gradient MCMC with Elastic Coupling| Jost Tobias Springenberg, Aaron Klein, Stefan Falkner and Frank Hutter| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_30.pdf
  26. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables|Chris J. Maddison, Andriy Mnih and Yee Whye Teh| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_31.pdf
  27. Known Unknowns: Uncertainty Quality in Bayesian Neural Networks | Ramon Oliveira, Pedro Tabacof and Eduardo Valle| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_32.pdf
  28. Normalizing Flows on Riemannian Manifolds |Mevlana Gemici, Danilo Rezende and Shakir Mohamed|2016
    Source: http://bayesiandeeplearning.org/papers/BDL_33.pdf
  29. Posterior Distribution Analysis for Bayesian Inference in Neural Networks| Pavel Myshkov and Simon Julier| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_34.pdf
  30. Deep Bayesian Active Learning with Image Data| Yarin Gal, Riashat Islam and Zoubin Ghahramani| 2016
    Stefan Webb| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_35.pdf
  31. Bottleneck Conditional Density Estimators|Rui Shu, Hung Bui and Mohammad Ghavamzadeh| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_36.pdf
  32. A Tighter Monte Carlo Objective with Renyi alpha-Divergence Measures| Stefan Webb and Yee Whye Teh| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_37.pdf
  33. Bayesian Neural Networks for Predicting Learning Curves| Aaron Klein, Stefan Falkner, Jost Tobias Springenberg and Frank Hutter| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_38.pdf
  34. Nested Compiled Inference for Hierarchical Reinforcement Learning|Tuan Anh Le, Atılım Güneş Baydin and Frank Wood|2016
    Source: http://bayesiandeeplearning.org/papers/BDL_41.pdf
  35. Open Problems for Online Bayesian Inference in Neural Networks | Robert Loftin and David Roberts| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_42.pdf
  36. Deep Probabilistic Programming| Dustin Tran, Matt Hoffman, Kevin Murphy, Rif Saurous, Eugene Brevdo, and David Blei| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_43.pdf
  37. Markov Chain Monte Carlo for Deep Latent Gaussian Models |Matthew Hoffman| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_44.pdf
  38. Semi-supervised Active Learning with Deep Probabilistic Generative Models | Amar Shah and Zoubin Ghahramani| 2016
    Source: http://bayesiandeeplearning.org/papers/BDL_43.pdf
  39. Thesis: Uncertainty in Deep Learning | Yarin Gal| PhD Thesis, 2016
    Source: http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf, Blog: http://mlg.eng.cam.ac.uk/yarin/blog_2248.html
  40. Deep survival analysis|R. Ranganath, A. Perotte, N. Elhadad, and D. Blei|2016
    Source: http://www.cs.columbia.edu/~blei/papers/RanganathPerotteElhadadBlei2016.pdf
  41. Towards Bayesian Deep Learning: A Survey| Hao Wang, Dit-Yan Yeung|2016
    Source: https://arxiv.org/pdf/1604.01662

2017

  1. Dropout Inference in Bayesian Neural Networks with Alpha-divergences |Yingzhen Li, Yarin Gal|2017
    Source: https://arxiv.org/abs/1703.02914
  2. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? |Alex Kendall, Yarin Gal|2017
    Source: https://arxiv.org/abs/1703.04977
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