BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/New_York X-WR-TIMEZONE:America/New_York BEGIN:VEVENT UID:575@fds.yale.edu DTSTART;TZID=America/New_York:20230327T160000 DTEND;TZID=America/New_York:20230327T170000 DTSTAMP:20250916T142121Z URL:https://fds.yale.edu/events/sds-colloquium-nadav-cohen-tel-aviv-univer sity-what-makes-data-suitable-for-deep-learning/ SUMMARY:S&\;DS Colloquium: Nadav Cohen (Tel Aviv University) "What Makes Data Suitable for Deep Learning?" DESCRIPTION:Deep learning is delivering unprecedented performance when appl ied to various data modalities\, yet there are data distributions over whi ch it utterly fails. The question of what makes a data distribution suitab le for deep learning is a fundamental open problem in the field.  In this talk I will present a recent theory aiming to address the problem via too ls from quantum physics.  The theory establishes that certain neural netw orks are capable of accurate prediction over a data distribution if and on ly if the data distribution admits low quantum entanglement under certain partitions of features.  This brings forth practical methods for adaptati on of data to neural networks\, and vice versa.  Experiments with widespr ead models over various datasets will demonstrate the findings.  An under lying theme of the talk will be the potential of physics to advance our un derstanding of the relation between deep learning and real-world data.\n\n \nWorks covered in the talk were in collaboration with my graduate student s Noam Razin\, Yotam Alexander\, Nimrod De La Vega and Tom Verbin.\n\n\n\n Bio: Nadav Cohen is an Asst. Prof. of Computer Science at Tel Aviv Univers ity.  His research focuses on the theoretical and algorithmic foundations of deep learning.  He earned a BSc in electrical engineering and a BSc i n mathematics (both summa cum laude) at the Technion Excellence Program fo r Distinguished Undergraduates\, followed by a PhD (direct track) in compu ter science at the Hebrew University of Jerusalem.  Subsequently\, he was a postdoctoral research scholar at the Institute for Advanced Study in Pr inceton.  For his contributions to deep learning\, Nadav received a numbe r of awards\, including the Google Doctoral Fellowship in Machine Learning \, the Rothschild Postdoctoral Fellowship\, the Zuckerman Postdoctoral Fel lowship\, and the Google Research Scholar Award.\n\n\n\nIn-Person seminars will be held at Mason Lab 211\, 9 Hillhouse Avenue with the option of vir tual participation: https://yale.hosted.panopto.com/Panopto/Pages/Viewer. aspx?id=53e6ca36-44bf-4760-83fc-af93011fd562\n\n\n\n3:30pm -   Pre-talk m eet and greet teatime - Dana House\, 24 Hillhouse Avenue\n CATEGORIES:FDS Events,Statistics & Data Science Seminar,Colloquium,Seminar Series END:VEVENT BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR