BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Petar Veličković (DeepMind and University of Cambridge) DTSTART:20220929T160000Z DTEND:20220929T170000Z DTSTAMP:20260423T003242Z UID:MPML/78 DESCRIPTION:Title: Ge ometric Deep Learning: Grids\, Graphs\, Groups\, Geodesics and Gauges\ nby Petar Veličković (DeepMind and University of Cambridge) as part of M athematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nTh e last decade has witnessed an experimental revolution in data science and machine learning\, epitomised by deep learning methods. Indeed\, many hig h-dimensional learning tasks previously thought to be beyond reach –such as computer vision\, playing Go\, or protein folding – are in fact feas ible with appropriate computational scale. Remarkably\, the essence of dee p learning is built from two simple algorithmic principles: first\, the no tion of representation or feature learning\, whereby adapted\, often hiera rchical\, features capture the appropriate notion of regularity for each t ask\, and second\, learning by local gradient-descent type methods\, typic ally implemented as backpropagation.\n\nWhile learning generic functions i n high dimensions is a cursed estimation problem\, most tasks of interest are not generic\, and come with essential pre-defined regularities arising from the underlying low-dimensionality and structure of the physical worl d. This talk is concerned with exposing these regularities through unified geometric principles that can be applied throughout a wide spectrum of ap plications.\n\nSuch a 'geometric unification' endeavour in the spirit of F elix Klein's Erlangen Program serves a dual purpose: on one hand\, it prov ides a common mathematical framework to study the most successful neural n etwork architectures\, such as CNNs\, RNNs\, GNNs\, and Transformers. On t he other hand\, it gives a constructive procedure to incorporate prior phy sical knowledge into neural architectures and provide principled way to bu ild future architectures yet to be invented.\n LOCATION:https://researchseminars.org/talk/MPML/78/ END:VEVENT END:VCALENDAR