Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks-Reference-Cited by-同舟云学术

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Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks

Published:2020-05 Issue:1 Volume:1 Page:84-105
ISSN:2641-8770
Container-title:IEEE Journal on Selected Areas in Information Theory
language:
Short-container-title:IEEE J. Sel. Areas Inf. Theory

Author:

Oymak Samet^ORCID,Soltanolkotabi Mahdi^ORCID

Funder

NSF

Packard Fellowship in Science and Engineering

Sloan Research Fellowship in Mathematics

NSF-CAREER

Air Force Office of Scientific Research Young Investigator Program

DARPA Learning with Less Labels and Fast Network Interface Cards Programs

NSF-CIF

Google Faculty Research Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

https://ieeexplore.ieee.org/ielam/8700143/8768428/9081945-aam.pdf

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1. Convergence of a quasi-Newton method for solving systems of nonlinear underdetermined equations;Computational Optimization and Applications;2024-09-06

2. Learning Time-Scales in Two-Layers Neural Networks;Foundations of Computational Mathematics;2024-08-22

3. Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification;Journal of Biomedical Informatics;2024-08

4. Memory Capacity of Two Layer Neural Networks with Smooth Activations;SIAM Journal on Mathematics of Data Science;2024-07-16

5. On the Training and Generalization of Deep Operator Networks;SIAM Journal on Scientific Computing;2024-07-01

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