BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Daniel Platt (KCL) DTSTART:20230503T090000Z DTEND:20230503T100000Z DTSTAMP:20260423T021032Z UID:CompAlg/16 DESCRIPTION:Title: Group invariant machine learning by fundamental domain projections\nb y Daniel Platt (KCL) as part of Machine Learning Seminar\n\n\nAbstract\nIn many applications one wants to learn a function that is invariant under a group action. For example\, classifying images of digits\, no matter how they are rotated. There exist many approaches in the literature to do this . I will mention two approaches that are very useful in many applications\ , but struggle if the group is big or acts in a complicated way. I will th en explain our approach which does not have these two problems. The approa ch works by finding some "canonical representative" of each input element. In the example of images of digits\, one may rotate the digit so that the brightest quarter is in the top-left\, which would define a "canonical re presentative". In the general case\, one has to define what that means. Ou r approach is useful if the group is big\, and I will present experiments on the Complete Intersection Calabi-Yau and Kreuzer-Skarke datasets to sho w this. Our approach is useless if the group is small\, and the case of ro tated images of digits is an example of this. This is joint work with Benj amin Aslan and David Sheard.\n LOCATION:https://researchseminars.org/talk/CompAlg/16/ END:VEVENT END:VCALENDAR