Deep learning algorithms attempt to discover good representations at multiple levels of abstraction. This research area has seen an explosion of progress over the past several years, with significant algorithmic advances as well as applications to areas as diverse as vision, speech processing, language understanding, robotics, game playing, neuroscience, and health. Major machine learning and AI conferences often dedicate several sessions to deep learning, attesting to the widespread interest in this area of research. The rapid (and perhaps bewildering) rate of progress also opens up new research challenges. This symposium aims to bring together researchers in deep learning and related areas to discuss the new advances and challenges and to brainstorm about new solutions and directions.


We will follow the procedure which was used successfully at the NIPS 2015 Deep Learning Symposium. Please note that this is very different from the usual workshop and conference submission/reviewing process, but closer to the good old method of inviting selected speakers to talk in a workshop, now using a web-based social network.

We have invited a small number of leading experts in the field to serve as our Program Committee (PC). PC members can recommend papers and speakers (selected outside their own groups and conflict of interest) for the Symposium, based on papers they have read or talks they have attended recently. There will be no formal review process to select papers. Instead, papers and talks will be selected based solely on our PCs recommendations.

The schedule will be divided into several parts, grouping work by area. Each talk will be followed by a Q&A session, with questions taken from the audience, our PC members as well as via public polls which will be opened a week before the event. In addition to invited talks, we will also have panel discussions concluding each session, bringing together the invited speakers, the organizers and leaders in the field.


Yoshua Bengio University of MontrealRoger Grosse University of TorontoNavdeep Jaitly Google BrainYann Le Cun Facebook AI Research, New York University


Azalia Mirhoseni -- Google Brain

Anelia Angelova -- Google Brain

Francis Bach -- INRIA

Samy Bengio -- Google Brain

James Bergstra -- Kindred

Phil Blunsom -- Oxford and Google DeepMind

Kyunghyun Cho -- NYU

Adam Coates -- Baidu

Aaron Courville -- U de Montreal

Trevor Darrell -- UC Berkeley

Nando de Freitas -- Oxford and Google DeepMind

Ian Goodfellow -- OpenAI

Raia Hadsell -- Google DeepMind

Andrej Karpathy -- OpenAI

Diederik Kingma -- OpenAI

Brian Kingsbury -- IBM

Hugo Larochelle -- Twitter Cortex

Honglak Lee -- Univ. of Michigan

Sergey Levine -- UC Berkeley

Roland Memisevic -- U de Montreal, TwentyBN

Vlad Mnih -- Google DeepMind

Doina Precup -- McGill University

Ruslan Salakhutdinov -- CMU

Jascha Sohl-Dickstein -- Google Brain

Christian Szegedy -- Google Brain

Graham Taylor -- Univ. of Guelph

Raquel Urtasun -- Univ. of Toronto

Jason Weston -- Facebook

Chris Williams -- Univ. of Edinburgh

Andrew Zisserman -- Oxford and Google DeepMind