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VERSION:2.0
PRODID:-//Superpower Daily//AI Events//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Superpower Daily AI events
BEGIN:VEVENT
UID:7bf0f5e8-1dff-4caa-ae44-13fce4f5e272@superpowerdaily.com
DTSTAMP:20261017T140356Z
LAST-MODIFIED:20261017T140356Z
DTSTART:20261022T190000Z
DTEND:20261022T200000Z
SUMMARY:Neural Network Scaling Limits: From Theory to Practice
DESCRIPTION:Princeton ORFE Associate Professor and 2024 Sloan Mathematics F
 ellow Boris Hanin will survey how neural network scaling limits can connec
 t deep learning theory with practical predictions for large-scale models. 
 His talk will explore how architecture\, data\, and optimization shape lea
 rning as model and dataset sizes grow.\nDetails: https://superpowerdaily.c
 om/events/neural-network-scaling-limits-from-theory-to-practice-7bf0f5e8-1
 dff-4caa-ae44-13fce4f5e272\nRegister: https://events.seas.harvard.edu/even
 t/neural-network-scaling-limits-from-theory-to-practice
LOCATION:Cambridge\, MA
URL:https://superpowerdaily.com/events/neural-network-scaling-limits-from-t
 heory-to-practice-7bf0f5e8-1dff-4caa-ae44-13fce4f5e272
STATUS:TENTATIVE
TRANSP:TRANSPARENT
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