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TZID:(UTC+01:00) Amsterdam\, Berlin\, Bern\, Rome\, Stockholm\, Vienna
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SUMMARY:SPCL_Bcast: Petar Veličković\, Capturing Computation with Algori
 thmic Alignment
DTSTART:20240321T180000
DTEND:20240321T190000
DTSTAMP:20231204T160612Z
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 00100000005F5B2B19D958FF4FA6DBC77B6BEF8652
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DESCRIPTION:https://spcl.inf.ethz.ch/Bcast/join\n\nAbstract: What makes a 
 neural network better\, or worse\, at fitting certain tasks? This question
  is arguably at the heart of neural network architecture design\, and it i
 s remarkably hard to answer rigorously. Over the past few years\, there ha
 ve been a plethora of attempts\, using various facets of advanced mathemat
 ics\, to answer this question under various assumptions. One of the most s
 uccessful directions -- algorithmic alignment -- assumes that the target f
 unction\, and a mechanism for computing it\, are completely well-defined a
 nd known (i.e. the target is to learn to execute an algorithm). In this se
 tting\, fitting a task is equated to capturing the computations of an algo
 rithm\, inviting analyses from diverse branches of mathematics and compute
 r science. I will present some of my personal favourite works in algorithm
 ic alignment\, along with their implications for building intelligent syst
 ems of the future.\n\nSee https://spcl.inf.ethz.ch/Bcast/ for more talks a
 nd info.\n
LOCATION;LANGUAGE=en-US:
ORGANIZER:MAILTO:marcin.chrapek@inf.ethz.ch
PRIORITY:5
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