Call for papers

We invite researchers to submit work in any of the following areas:

  • Uncertainty in deep learning,
  • probabilistic deep models (such as extensions and application of Bayesian neural networks),
  • deep probabilistic models (such as hierarchical Bayesian models and their applications),
  • deep generative models (such as variational autoencoders),
  • practical approximate inference techniques in Bayesian deep learning,
  • connections between deep learning and Gaussian processes,
  • applications of Bayesian deep learning,
  • or any of the topics below.

A submission should take the form of an extended abstract (3 pages long) in PDF format using the NeurIPS 2019 style. Author names do not need to be anonymized, and conflicts of interest in assessing submitted contributions will be based on this (reviewers will not be involved in the assessment of a submission by authors within the same institution). References may extend as far as needed beyond the 3 page upper limit. Submissions may extend beyond the 3 pages upper limit, but reviewers are not expected to read beyond the first 3 pages. If the research has previously appeared in a journal, workshop, or conference (including the NeurIPS 2019 conference), the workshop submission should extend that previous work. Dual submissions to ICLR 2019 and AISTATS 2019 are permitted.

Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by September 9, 2019; submission page is here. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings). Notification of acceptance will be October 1, 2019.

Key Dates:

  • Extended abstract submission deadline: September 9, 2019 (midnight AOE) (submission page is here)
  • Acceptance notification: October 1, 2019
  • Camera ready submission: 30 October 2019
  • Workshop: December 13 or 14, 2019

We will do our best to guarantee workshop registration for all accepted workshop submissions


While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community over the past few years, with the introduction of new deep learning models that take advantage of Bayesian techniques, as well as Bayesian models that incorporate deep learning elements [1-11]. In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s’, in seminal works by Radford Neal [12], David MacKay [13], and Dayan et al. [14]. These gave us tools to reason about deep models’ confidence, and achieved state-of-the-art performance on many tasks. However earlier tools did not adapt when new needs arose (such as scalability to big data), and were consequently forgotten. Such ideas are now being revisited in light of new advances in the field, yielding many exciting new results.

Extending on last year’s workshop’s success, this workshop will again study the advantages and disadvantages of such ideas, and will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. It will be composed of five themes: deep generative models, variational inference using neural network recognition models, practical approximate inference techniques in Bayesian neural networks, applications of Bayesian neural networks, and information theory in deep learning. Future directions for the field will be debated in a panel discussion.

Previous workshops:

Our 2018 workshop page is available here; Our 2017 workshop page is available here; Our 2016 workshop page is available here; videos from the 2016 workshop are available online as well.


  • Uncertainty in deep learning,
  • Applications of Bayesian deep learning,
  • Probabilistic deep models (such as extensions and application of Bayesian neural networks),
  • Deep probabilistic models (such as hierarchical Bayesian models and their applications),
  • Generative deep models (such as variational autoencoders),
  • Information theory in deep learning,
  • Deep ensemble uncertainty,
  • NTK and Bayesian modelling,
  • Connections between NNs and GPs,
  • Incorporating explicit prior knowledge in deep learning (such as posterior regularisation with logic rules),
  • Approximate inference for Bayesian deep learning (such as variational Bayes / expectation propagation / etc. in Bayesian neural networks),
  • Scalable MCMC inference in Bayesian deep models,
  • Deep recognition models for variational inference (amortised inference),
  • Bayesian deep reinforcement learning,
  • Deep learning with small data,
  • Deep learning in Bayesian modelling,
  • Probabilistic semi-supervised learning techniques,
  • Active learning and Bayesian optimisation for experimental design,
  • Kernel methods in Bayesian deep learning,
  • Implicit inference,
  • Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general.


Confirmed Speakers

  • Debora Marks (Harvard Medical School, protein structure prediction using sparse BDL)
  • Jasper Snoek (Google, deep generative models for sequence data)
  • Chelsea Finn (Google Brain / Berkeley / Stanford, meta-learning as hierarchical Bayes)
  • Andrew Gordon Wilson (Cornell, Bayesian model averaging in deep learning)
  • Yingzhen Li (Microsoft Research, variational implicit processes)
  • Roger Grosse (Toronto, functional Bayesian neural networks)
  • Alexander G. de G. Matthews (DeepMind, Gaussian Process Behaviour in Wide Deep Neural Networks)


  1. Kingma, DP and Welling, M, ‘’Auto-encoding variational bayes’’, 2013.
  2. Rezende, D, Mohamed, S, and Wierstra, D, ‘’Stochastic backpropagation and approximate inference in deep generative models’’, 2014.
  3. Blundell, C, Cornebise, J, Kavukcuoglu, K, and Wierstra, D, ‘’Weight uncertainty in neural network’’, 2015.
  4. Hernandez-Lobato, JM and Adams, R, ’’Probabilistic backpropagation for scalable learning of Bayesian neural networks’’, 2015.
  5. Gal, Y and Ghahramani, Z, ‘’Dropout as a Bayesian approximation: Representing model uncertainty in deep learning’’, 2015.
  6. Gal, Y and Ghahramani, G, ‘’Bayesian convolutional neural networks with Bernoulli approximate variational inference’’, 2015.
  7. Kingma, D, Salimans, T, and Welling, M. ‘’Variational dropout and the local reparameterization trick’’, 2015.
  8. Balan, AK, Rathod, V, Murphy, KP, and Welling, M, ‘’Bayesian dark knowledge’’, 2015.
  9. Louizos, C and Welling, M, “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors”, 2016.
  10. Lawrence, ND and Quinonero-Candela, J, “Local distance preservation in the GP-LVM through back constraints”, 2006.
  11. Tran, D, Ranganath, R, and Blei, DM, “Variational Gaussian Process”, 2015.
  12. Neal, R, ‘’Bayesian Learning for Neural Networks’’, 1996.
  13. MacKay, D, ‘’A practical Bayesian framework for backpropagation networks‘’, 1992.
  14. Dayan, P, Hinton, G, Neal, R, and Zemel, S, ‘’The Helmholtz machine’’, 1995.
  15. Wilson, AG, Hu, Z, Salakhutdinov, R, and Xing, EP, “Deep Kernel Learning”, 2016.
  16. Saatchi, Y and Wilson, AG, “Bayesian GAN”, 2017.
  17. MacKay, D.J.C. “Bayesian Methods for Adaptive Models”, PhD thesis, 1992.