BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Anindita Maiti (Northeastern University) DTSTART:20220912T140000Z DTEND:20220912T150000Z DTSTAMP:20260423T020953Z UID:CompAlg/3 DESCRIPTION:Title: Non-perturbative Non-Lagrangian Neural Network Field Theories\nby Anin dita Maiti (Northeastern University) as part of Machine Learning Seminar\n \n\nAbstract\nEnsembles of Neural Network (NN) output functions describe f ield theories. The Neural Network Field Theories become free i.e. Gaussian in the limit of infinite width and independent parameter distributions\, due to Central Limit Theorem (CLT). Interaction terms i.e. non-Gaussianiti es in these field theories arise due to violations of CLT at finite width and / or correlated parameter distributions. In general\, non-Gaussianitie s render Neural Network Field Theories as non-perturbative and non-Lagrang ian. In this talk\, I will describe methods to study non-perturbative non- Lagrangian field theories in Neural Networks\, via a dual framework over p arameter distributions. This duality lets us study correlation functions a nd symmetries of NN field theories in the absence of an action\; further t he partition function can be approximated as a series sum over connected c orrelation functions. Thus\, Neural Networks allow us to study non-perturb ative non-Lagrangian field theories through their architectures\, and can be beneficial to both Machine Learning and physics.\n LOCATION:https://researchseminars.org/talk/CompAlg/3/ END:VEVENT END:VCALENDAR