Artificial Intelligence

Weaving Together Machine Learning, Theoretical Physics, and Neuroscience

When:
Friday, May 21, 2021, 11:00am - 12:00pm PDTiCal
Where:
Virtual Via Zoom Webinar
This event is open to the public.
Type:
AI Seminar
Speaker:
Surya Ganguli (Stanford University)
Video:
https://youtu.be/f1ryBuwGb9k
Description:

AI Seminar Series

Seminars for the Artificial Intelligence Division at USC Information Sciences Institute

YOU ONLY NEED TO REGISTER ONCE TO ATTEND THE ENTIRE SERIES - We will send you email announcements with details of the upcoming speakers.

Register in advance for this webinar:

https://usc.zoom.us/webinar/register/WN__0VhakI6Q6i3JsasdmNWcA

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Abstract: An exciting area of intellectual activity in this century may well revolve around a synthesis of machine learning, theoretical physics, and neuroscience.  The unification of these fields will likely enable us to exploit the power of complex systems analysis, developed in theoretical physics and applied mathematics, to elucidate the design principles governing neural systems, both biological and artificial, and deploy these principles to develop better algorithms in machine learning.  We will give several vignettes in this direction, including:  (1) determining the best optimization problem to solve in order to perform regression in high dimensions;  (2) finding exact solutions to the dynamics of generalization error in deep linear networks; (3) derving the detailed structure of the primate retina by analyzing optimal convolutional auto-encoders of natural movies; (4) analyzing and explaining the origins of hexagonal firing patterns in recurrent neural networks trained to path-integrate; (5) understanding the geometry and dynamics of high dimensional optimization in the classical limit of dissipative many-body quantum optimizers.

Bio: Surya Ganguli triple majored in physics, mathematics, and EECS at MIT, completed a PhD in string theory at Berkeley, and a postdoc in theoretical neuroscience at UCSF. He is now an associate professor of Applied physics at Stanford where he leads the Neural Dynamics and Computation Lab. His research spans the fields of neuroscience, machine learning and physics, focusing on understanding and improving how both biological and artificial neural networks learn striking emergent computations.  He has been awarded a Swartz-Fellowship in computational neuroscience, a Burroughs-Wellcome Career Award, a Terman Award, a NeurIPS Outstanding Paper Award, a Sloan fellowship, a James S. McDonnell Foundation scholar award in human cognition, a McKnight Scholar award in Neuroscience, a Simons Investigator Award in the mathematical modeling of living systems, and an NSF career award. 

Host: Keith Burghardt, POC: Pete Zamar

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