Understanding neural network sample complexity and interpretable convergence-guaranteed deep learning with polynomial regression

Understanding neural network sample complexity and interpretable convergence-guaranteed deep learning with polynomial regression

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020

Bibliographic Details
Main Author: Emschwiller, Matt V.
Other Authors: David Gamarnik.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2020
Subjects:
Operations Research Center.
Online Access:https://hdl.handle.net/1721.1/127290

Internet

https://hdl.handle.net/1721.1/127290

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