In recent years, random matrix theory (RMT) has come to the forefront of learning theory as a tool to understand some of its most important challenges. From generalization of deep learning models to a precise analysis of optimization algorithms, RMT provides analytically tractable models.
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Mon 12:00 p.m. - 12:05 p.m.
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Live Intro
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Introduction by moderator
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SlidesLive Video » |
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Mon 12:05 p.m. - 1:06 p.m.
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Introduction
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Tutorial
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SlidesLive Video » |
Fabian Pedregosa · Courtney Paquette 🔗 |
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Mon 1:06 p.m. - 1:30 p.m.
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Q&A
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Live Q&A
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Mon 1:30 p.m. - 2:00 p.m.
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Analysis of numerical algorithms
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Tutorial
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SlidesLive Video » |
Thomas Trogdon 🔗 |
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Mon 2:00 p.m. - 2:15 p.m.
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Q&A
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Live Q&A
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Mon 2:15 p.m. - 2:45 p.m.
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The Mystery of Generalization: Why Does Deep Learning Work?
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Tutorial
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SlidesLive Video » |
Jeffrey Pennington 🔗 |
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Mon 2:45 p.m. - 3:00 p.m.
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Q&A
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Live Q&A
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