NSF award 2601870 · Brown University

Collaborative Research: MFAI:Random geometry of the empirical risk landscape in deep learning: Optimization and Generalization

Final report due

28 November 2029

in 1,137 days · The award ends on 31 July 2029.

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No-cost extension notice21 Jul 2029in 1007 days
award ending 2029-07-31 · Research.gov
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Final report due28 Nov 2029in 1137 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2029in 1137 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2029in 1137 days
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PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 July 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 2029.

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Award card

Award number: 2601870
Period of support: 1 Aug 2026 – 31 Jul 2029
Amount: $450,000
Awardee: Brown University, Providence, RI
Program: OFFICE OF MULTIDISCIPLINARY AC, Special Projects - CCF, MSPA-INTERDISCIPLINARY
Directorate: MPS

Abstract

Modern deep learning models often have far more parameters than training examples, and they can fit training data perfectly even when the data contain noise. Classical theory would suggest that such models should perform poorly on new data, yet in practice they often generalize well. They are also successfully trained using simple methods such as gradient descent, even though the underlying optimization problems are…

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