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SUMMARY:Distilling ML Models into Formulae for Ricci-Flat Metrics - Viktor
  Mirjanic\, University of Cambridge
DTSTART:20250210T123000Z
DTEND:20250210T130000Z
UID:TALK227632@talks.cam.ac.uk
CONTACT:Sam Nallaperuma-Herzberg
DESCRIPTION:Machine learning has shown great success in approximating Ricc
 i-flat metrics on Calabi–Yau manifolds\, but its black-box nature often 
 limits interpretability. In this talk\, I will show that for highly symmet
 ric manifolds\, the machine learning models used to approximate these metr
 ics can be distilled into closed-form symbolic expressions. These expressi
 ons are compact\, interpretable\, and have the same accuracy as the origin
 al model.
LOCATION:FW11\, Willam Gates building (Department of Computer Science and 
 Technology)
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