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SUMMARY:Nick Vannieuwenhoven (KU Leuven)
DTSTART:20230308T100000Z
DTEND:20230308T110000Z
DTSTAMP:20260423T021102Z
UID:CompAlg/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/7/">
 Group-invariant tensor train networks for supervised learning</a>\nby Nick
  Vannieuwenhoven (KU Leuven) as part of Machine Learning Seminar\n\n\nAbst
 ract\nInvariance under selected transformations has recently proven to be 
 a powerful inductive bias in several machine learning models. One class of
  such models are tensor train networks. In this talk\, we impose invarianc
 e relations on tensor train networks. We introduce a new numerical algorit
 hm to construct a basis of tensors that are invariant under the action of 
 normal matrix representations of an arbitrary discrete group. This method 
 can be up to several orders of magnitude faster than previous approaches. 
 The group-invariant tensors are then combined into a group-invariant tenso
 r train network\, which can be used as a supervised machine learning model
 . We applied this model to a protein binding classification problem\, taki
 ng into account problem-specific invariances\, and obtained prediction acc
 uracy in line with state-of-the-art invariant deep learning approaches. Th
 is is joint work with Brent Sprangers.\n
LOCATION:https://researchseminars.org/talk/CompAlg/7/
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