Events
Colloquium
Scaling Neural Networks: Laws and Limits
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Speaker: Cengiz Pehlavan (Harvard) Assistant Professor of Applied Mathematics Harvard University Wednesday, March 4, 2026 11:30AM - 1:00PM Lunch at 11:30am in 1307
Talk 12:00-1:00pm in 1327 Location: Yale Institute for Foundations of Data Science & Webcast, 219 Prospect Street, 13th Floor, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=cd4449de-8508-4184-aa35-b3ca01484881 |
Abstract: Scaling up neural network models has enabled unprecedented capabilities through learning, but we still lack the first-principles understanding needed to ensure their safety, reliability, and efficiency. I will show how tools from statistical mechanics and random matrix theory allow us to analyze neural networks in appropriate infinite-size scaling limits, thereby mapping the learning regimes that govern observed scaling behavior. These results account for the main features of empirical neural scaling laws; enable transfer of near-optimal hyperparameters across model sizes, yielding significant computational benefits; and provide a framework for understanding emergent behaviors such as in-context learning.
Speaker Bio: Cengiz (pronounced “Jen·ghiz”) comes to Harvard SEAS from the Flatiron Institute’s Center for Computational Biology (CCB), where he was a a research scientist in the neuroscience group. Before CCB, Cengiz was a postdoctoral associate at Janelia Research Campus, and before that a Swartz Fellow at Harvard.
Cengiz received a doctorate in physics from Brown University and undergraduate degrees in physics and electrical engineering from Bogazici University. He is a native of Tosya, Turkey.
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