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Colloquium
Neural Network Scaling Limits: From Theory to Practice
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Speaker: Boris Hanin (Princeton) Associate Professor of Operations Research and Financial Engineering Princeton University Wednesday, October 14, 2026 12:00PM - 1:00PM Lunch 11:30am-12:00pm
Talk 12:00pm-1:00pm |
Abstract: A fundamental question in deep learning is to describe the scaling limits of neural networks by answering how and what neural networks learn in regimes where both model and dataset size diverge. From the perspective of theory the goal is to identify what infinite size limits of neural network training dynamics exist and to analyze the mechanisms by which architecture and optimizer combine to learn data-dependent features. The aspiration is to develop an analog of the ideal gas law, where out of the collective motion of many weakly interacting molecules (in this analogy network parameters) emerge a small number of thermodynamic variables like pressure, temperature, and volume that give simple and predictive insights into the properties of realistic gasses. From the practical perspective the objective is to design a wind tunnel for neural networks. The goal is to predict reliably from experiments on small models and datasets the most performant ways to align architecture, data, and optimizer at large scale. In this talk I will give a survey of some of my work on neural network scaling limits emphasizing the interplay between theory and practice.
Speaker Bio: I am an Associate Professor at Princeton ORFE (and Associated Faculty at Princeton PACM) working on deep learning, probability, and spectral asymptotics. Prior to Princeton, I was an Assistant Professor in Mathematics at Texas A&M, an NSF Postdoc at MIT Math, and a PhD student in Math at Northwestern, where I was supervised by Steve Zelditch.
I am one of the co-organizers of the Princeton Probability Seminar and the alg-ML seminar.
I am an advisor at Mithril, Logical Intelligence, Lumos Health, and 9D Labs.
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