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SUMMARY:Charlotte Aten (Denver)
DTSTART:20230906T140000Z
DTEND:20230906T150000Z
DTSTAMP:20260423T035418Z
UID:CompAlg/24
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/24/"
 >Discrete neural nets and polymorphic learning</a>\nby Charlotte Aten (Den
 ver) as part of Machine Learning Seminar\n\n\nAbstract\nClassical neural n
 etwork learning techniques have primarily been focused on optimization in 
 a continuous setting. Early results in the area showed that many activatio
 n functions could be used to build neural nets that represent any function
 \, but of course this also allows for overfitting. In an effort to amelior
 ate this deficiency\, one seeks to reduce the search space of possible fun
 ctions to a special class which preserves some relevant structure. I will 
 propose a solution to this problem of a quite general nature\, which is to
  use polymorphisms of a relevant discrete relational structure as activati
 on functions. I will give some concrete examples of this\, then hint that 
 this specific case is actually of broader applicability than one might gue
 ss.\n
LOCATION:https://researchseminars.org/talk/CompAlg/24/
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