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SUMMARY:Edward Pearce-Crump (Imperial)
DTSTART:20230621T090000Z
DTEND:20230621T100000Z
DTSTAMP:20260423T035418Z
UID:CompAlg/18
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/18/"
 >Exploring group equivariant neural networks using set partition diagrams<
 /a>\nby Edward Pearce-Crump (Imperial) as part of Machine Learning Seminar
 \n\n\nAbstract\nWhat do jellyfish and an 11th century Japanese novel have 
 to do with neural networks? In recent years\, much attention has been give
 n to developing neural network architectures that can efficiently learn fr
 om data with underlying symmetries. These architectures ensure that the le
 arned functions maintain a certain geometric property called group equivar
 iance\, which determines how the output changes based on a change to the i
 nput under the action of a symmetry group. In this talk\, we will describe
  a number of new group equivariant neural network architectures that are b
 uilt using tensor power spaces of $\\mathbb{R}^n$ as their layers. We will
  show that the learnable\, linear functions between these layers can be ch
 aracterised by certain subsets of set partition diagrams. This talk will b
 e based on several papers that are to appear in ICML 2023.\n
LOCATION:https://researchseminars.org/talk/CompAlg/18/
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