Friday, December 4, 2020
10:00 AM -
3:30 PM EST
We are in the midst of a revolution driven by models for learning in neural networks. In this symposium we will explore these models, their application to the deep networks that are the state of the art in language processing by machines, and the relation of these ideas to the extraordinary ability of human infants to learn language.
10:00 - 11:30
Learning to understand: Statistical learning and infant language development
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Jenny Saffran, University of Wisconsin
12:00 - 1:30
A mathematical theory of learning in deep neural networks
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Surya Ganguli, Stanford University and Facebook AI Research
2:00 - 3:30
Neural scaling laws and GPT-3
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Jared Kaplan, Johns Hopkins University and OpenAI
This event is sponsored in part by the Center for the Physics of Biological Function, a joint effort of The Graduate Center, CUNY and Princeton University. To learn more about CPBF, visit biophysics.princeton.edu.