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Workshop
Bayesian Deep Learning
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling

Fri Dec 13 08:00 AM -- 06:45 PM (PST) @ West Exhibition Hall C
Event URL: http://bayesiandeeplearning.org »

Extending on the workshop’s success from the past 3 years, this workshop will study the developments in the field of Bayesian deep learning (BDL) over the past year. The workshop 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. Future directions for the field will be debated in a panel discussion.

Speakers:
* Andrew Wilson
* Deborah Marks
* Jasper Snoek
* Roger Grosse
* Chelsea Finn
* Yingzhen Li
* Alexander Matthews

Workshop summary:
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, with the introduction of new deep learning models that take advantage of Bayesian techniques, and Bayesian models that incorporate deep learning elements. Many ideas from the 1990s are now being revisited in light of recent advances in the fields of approximate inference and deep learning, yielding many exciting new results.

Author Information

Yarin Gal (University of Oxford)
José Miguel Hernández-Lobato (University of Cambridge)
Christos Louizos (University of Amsterdam)
Eric Nalisnick (University of Cambridge & DeepMind)
Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

Kevin Murphy (Google)
Max Welling (University of Amsterdam / Qualcomm AI Research)

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