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The Science of Deep Learning

March 13 - 14, 2019 
National Academy of Sciences, Washington, D.C. 

Organized by: David Donoho, Maithra Raghu, Ali Rahimi, Ben Recht and Matan Gavish

Artificial neural networks have re-emerged as a powerful concept for designing state-of-the-art algorithms in machine learning and artificial intelligence. Across a variety of fields, these architectures seem to outperform time-honored machine learning methods. Interestingly, our understanding of why and when these methods work remains limited. At the same time, an increasing number of mission-critical systems depend on deep neural networks, from autonomous vehicles to social media platforms that influence political discourse. Scientists are also beginning to rely more on deep learning as a knowledge discovery tool as research becomes ever more data driven.

This interdisciplinary meeting began with talks that surveyed the state of affairs in deep learning in academia and industry, the projected developments in the coming years, and the broader implications on science and society. The colloquium will then covered two timely, interleaved topics: First, what can deep learning do for science? What disciplines already integrating deep learning, and what lies ahead for scientists using deep learning? Second, what can science do for deep learning? What insights can deep learning gain from scientists who study complex systems (e.g. in Physics, Chemistry and the Life Sciences)? Can experimental techniques be used to study the nature of artificial deep neural networks? Can familiar principles that emerge in natural complex systems help us understand deep neural networks?


- A limited number of videos are available for public viewing.  More videos may be added as permission is received.

Wednesday March 13th

Session I: The State of Deep Learning (Chair: Donoho)

Opening remarks:  David Donoho, Stanford University

Overview talk (I) Amnon Shashua, Hebrew University / Mobileye, Successes and  Challenges in Modern Artificial Intelligence

Overview talk (II)  Jitendra Malik, University of California, Berkeley

Talk:  Chris Manning, Stanford University, The State of Deep Learning for Natural Language Processing

alk: Oriol Vinyals, Google AI, The State of Deep Reinforcement Learning

Critical Perspective: Strengths and fallacies in the dominant DL narrative

Moderator: David Donoho, Stanford University

Terrence Sejnowski, Salk Institute for Biological Studies

Tomaso Poggio, Massachusetts Institute of Technology

Regina Barzilay, Massachusetts Institute of Technology

Rodney Brooks, Massachusetts Institute of Technology

Session II: Deep Learning in Science (Chair: Raghu)

Talk: Regina Barzilay, Massachusetts Institute of Technology

Talk: Kyle Cranmer, New York University, Experiences with deep learning in particle physics

Talk:  Olga Troyanskaya, Princeton University

Talk:  Eero Simoncelli, New York University

Counterpoint: Bruno Olshausen, University of California, Berkeley, Can deep learning provide deep insight in neuroscience?

Counterpoint: Antonio Torralba, Massachusetts Institute of Technology

Panel Discussion: Scientific Funding for Deep Learning

Moderator: Juan Meza, NSF (TBC)

Robert Bonneau, DOD

Hava Siegelmann, DARPA

Henry Kautz, NSF

Richard (Doug) Riecken, USAF Office of Scientific Research


Annual Sackler Lecture

Introduction by Marcia McNutt, President, National Academy of Sciences

Rodney Brooks, Massachusetts Institute of Technology

Thursday March 14th

Session III: Theoretical Perspectives on Deep Learning (Chair: Rahimi)

Talk:  Tomaso Poggio, Massachusetts Institute of Technology

Deep learning: Solving the Approximation, Optimization and Generalization Puzzles 

Talk: Nati Srebro, Toyota Technological Institute at Chicago

Talk:  Peter Bartlett, University of California, Berkeley, Accurate prediction from interpolation: A new challenge for statistical learning theory

Counterpoint: Konrad Kording, University of Pennsylvania, Why neuroscience needs deep learning theory

Counterpoint: Anders Hansen, Cambridge University, On instabilities in deep learning - Does AI come at a cost?,

Counterpoint: Ronald Coifman, Yale University, Deeper Learning in Empirical Science, some requirements and needs

Critical Perspective: Could a good DL theory change practice?

Moderator: Ben Recht, UC Berkeley

Eero Simoncelli, New York University

Julia Kempe, New York University Center for Data Science

Policy and Science Funding Panel

Panel Discussion: Drivers and considerations for federal / industry space investment in fundamental academic AI research  

Moderator: Jim Kurose, NSF

John Beieler, IARPA

Juan Mesa, National Science Foundation

Tony Thrall, National Security Agency 

Session IV: Experimental Perspectives on Deep Learning (Chair: Gavish)

Short talk: Jonathon Phillips, National Institute of Standards and Technology, Data Sets for 

Analyzing Face Recognition Performance of Humans and Algorithms

Short talk: Isabelle Guyon, Paris-Sud University & ClopiNet, Neural Solvers for Power Transmission Problems

Talk:  Doina Precup, McGill University, From deep reinforcement learning to AI

Talk: Haim Sampolinsky, Hebrew University of Jerusalem, Theory-based measures of object representations in deep artificial and biological networks

Counterpoint: Tara Sainath, Google AI

Critical Perspective: What’s missing in today’s experimental analysis of DL?

Moderator: Jonathon Phillips, NIST

Jitendra Malik, University of California, Berkeley

Peter Bartlett, University of California, Berkeley

Antonio Torralba, Massachusetts Institute of Technology

Isabelle Guyon, Paris-Sud University & ClopiNet

Summary: Right ways forward? (Chair: Donoho)

Terrence Sejnowski, Salk Institute for Biological Studies

Jon Kleinberg, Cornell University

Leon Bottou, FaceBook AI Research, From Machine Learning to Artificial Intelligence

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