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X-WR-CALDESC:Events for ARNI
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TZOFFSETFROM:+0000
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DTSTART:20230101T000000
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DTSTART;TZID=UTC:20240429T113000
DTEND;TZID=UTC:20240429T130000
DTSTAMP:20260515T095104
CREATED:20240416T222403Z
LAST-MODIFIED:20240423T191956Z
UID:806-1714390200-1714395600@arni-institute.org
SUMMARY:Lenka Zdeborova (Seminar Speaker)
DESCRIPTION:Title: Phase transition in learning with neural networks  \nAbstract: Statistical physics has studied exactly solvable models of neural networks for more than four decades. In this talk\, we will put this line of work in perspective of recent questions stemming from deep learning. We will describe several types of phase transition that appear in the high-dimensional limit as a function of the amount of data. Discontinuous phase transitions are linked to adjacent algorithmic hardness. This so-called hard phase influences the behaviour of gradient-descent-like algorithms. We show a case where the hardness is mitigated by overparametrization\, proposing that the benefits of overparametrization may be linked to the usage of a specific type of algorithm. We will also discuss recent progress in identifying phase transitions and their consequences in networks with attention layers and in sampling with generative diffusion-based networks.
URL:https://arni-institute.org/event/lenka-zdeborova-seminar-speaker/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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