Schedule

08:30

COFFEE

08:50

John Hershey

John Hershey, Mitsubishi Electric Research Labs

Cracking the Cocktail Party Problem: Deep Clustering for Speech Separation

09:10

Daniel McDuff

Daniel McDuff, Affectiva

Emotion Intelligence to our Digital Experiences

09:30

Tony Jebara

Tony Jebara, Netflix

Double-cover Inference in Deep Belief Networks

09:50

Yoshua Bengio

Yoshua Bengio, Université de Montréal

Keynote: Deep Learning Frameworks

10:10

PANEL: What can be Done to Make Deep Learning as Impactful as Possible in the Near-Term?

10:30

Vivienne Sze

Vivienne Sze, MIT

Building Energy-Efficient Accelerators for Deep Learning

10:50

Hugo Larochelle

Hugo Larochelle, Google Brain

Applied Deep Learning: Now and Beyond

11:10

LUNCH

11:30

Honglak Lee

Honglak Lee, University of Michigan

Deep Learning with Disentangled Representations

11:50

COFFEE

12:10

WELCOME

SPEECH RECOGNITION

12:50

CONVERSATION & DRINKS

13:10

REGISTRATION

DEEP LEARNING FRAMEWORKS & EXPERIENCES

ACCELERATORS FOR DEEP LEARNING

14:10

Urs Köster

Urs Köster, Nervana Systems

Deep Learning at Scale

Yu-Hsin Chen

Yu-Hsin Chen, MIT

Building Energy-Efficient Accelerators for Deep Learning

14:50

Spyros Matsoukas

Spyros Matsoukas, Amazon

Deep Learning for Amazon Echo

15:10

Adam Lerer

Adam Lerer, Facebook

Learning Physical Intuition by Example

15:30

 Nathan Wilson

Nathan Wilson, Nara Logics

Biological Foundations for Deep Learning: Towards Decision Networks

NATURAL LANGUAGE UNDERSTANDING

16:10

Andrew McCallum

Andrew McCallum, University of Massachusetts Amherst

Deep Learning for Representation and Reasoning from Natural Language

Tejas Kulkarni

Tejas Kulkarni, DeepMind

Panelist

Olexandr Isayev

Olexandr Isayev, University of North Carolina

Panelist

Nanette Byrnes

Nanette Byrnes, MIT Technology Review

Moderator

Aditya Khosla

Aditya Khosla, MIT

Panelist

Jana Eggers

Jana Eggers, Nara Logics

Welcome

*Please note the agenda is subject to change

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