AI/ML Lecture & Networking @ Intuit

A monthly ML research colloquium by the New York Machine Learning Research Guild — this session in collaboration with our partner, Intuit. The Mathematical Theory of Deep Learning Arthur Jacot · Assistant Professor, Courant Institute of Mathematical Sciences, NYU Topic: TBD This session is a close-quarters look at that theory, with plenty of room to push back and dig in. About Arthur Arthur Jacot is an Assistant Professor at NYU's Courant Institute of Mathematical Sciences. He builds mathematical theory for how deep neural networks learn, and is best known for introducing the Neural Tangent Kernel during a PhD at EPFL with Clément Hongler. Recent work explains how DNNs act as a computational Occam's razor, finding simple, low-dimensional representations when trained with weight decay: A proof that wide networks trained with weight decay exhibit neural collapse (ICLR 2025, oral) An account of how DNNs break the curse of dimensionality through compositionality and symmetry learning…