BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Vasco Portilheiro (UCL)
DTSTART:20230412T150000Z
DTEND:20230412T160000Z
DTSTAMP:20260423T021039Z
UID:CompAlg/13
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/13/"
 >Barriers to Learning Symmetries</a>\nby Vasco Portilheiro (UCL) as part o
 f Machine Learning Seminar\n\n\nAbstract\nGiven the success of equivariant
  models\, there has been increasing interest in models which can learn a s
 ymmetry from data\, rather than it being imposed a priori. We present work
  which formalizes a tradeoff between (a) the simultaneous learnability of 
 symmetries and equivariant functions\, and (b) universal approximation of 
 equivariant functions. The work is motivated by an experiment which modifi
 es the Equivariant Multilayer Perceptron (EMLP) of Finzi et al. (2021) in 
 an attempt to learn a group together with an equivariant function. Additio
 nally\, the tradeoff is shown to not exist for group-convolutional network
 s.\n
LOCATION:https://researchseminars.org/talk/CompAlg/13/
END:VEVENT
END:VCALENDAR