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Date: December 10, 2016

Location: Centre Convencions Internacional Barcelona, Barcelona, Spain 

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

* Probabilistic deep models for classification and regression (such as extensions and application of Bayesian neural networks),

* Generative deep models (such as variational autoencoders),

* 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),

* Model uncertainty in deep learning,

* 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,

* Information theory in deep learning,

* Applying non-parametric methods, one-shot learning, and Bayesian deep learning in general.

A submission should take the form of an extended abstract (2 pages long) in PDF format using the NIPS style. Author names do not need to be anonymised and references may extend as far as needed beyond the 2 page upper limit. If research has previously appeared in a journal, workshop, or conference (including NIPS 2016 conference), the workshop submission should extend that previous work.

Submissions will be accepted as contributed talks or poster presentations. Extended abstracts should be submitted by 1 November 2016; submission details will be updated online towards the submission deadline. Final versions will be posted on the workshop website (and are archival but do not constitute a proceedings).

Extended abstract submission: *1 November 2016*

Acceptance notification: 16 November 2016

Travel award notification: 16 November 2016

Final paper submission: 5 December 2016

The workshop is endorsed by the International Society for Bayesian Analysis (ISBA), which will also provide a Travel Award to a graduate student or a junior researcher.

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.

In fact, the use of Bayesian techniques in deep learning can be traced back to the 1990s', in seminal works by Radford Neal, David MacKay, and Dayan et al.. 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.

This workshop will 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 will include a mix of invited talks, contributed talks, and contributed posters. Also, the historic context of key developments in the field will be explained in an invited talk, followed by a tribute talk to David MacKay's work in the field. Future directions for the field will be debated in a panel discussion.

Yarin Gal (University of Cambridge)

Christos Louizos (University of Amsterdam)

Zoubin Ghahramani (University of Cambridge)

Kevin Murphy (Google)

Max Welling (University of Amsterdam)