Anderson Acceleration for Distributed Training of Deep Learning Models-Reference-Cited by-同舟云学术

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Anderson Acceleration for Distributed Training of Deep Learning Models

Published:2022-03-26 Issue: Volume: Page:
ISSN:
Container-title:SoutheastCon 2022
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Author:

Pasini Massimiliano Lupo¹,Yin Junqi²,Reshniak Viktor³,Stoyanov Miroslav K.¹

Affiliation:

1. Oak Ridge National Laboratory,Computational Sciences and Engineering Division,Oak Ridge,USA

2. Oak Ridge National Laboratory,National Center for Computational Sciences Division,Oak Ridge,USA

3. Oak Ridge National Laboratory,Computer Science and Mathematics Division,Oak Ridge,USA

Funder

U.S. Department of Energy

Battelle

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9763896/9763889/09763953.pdf?arnumber=9763953

Reference42 articles.

1. Deep residual learning for image recognition;he;CoRR,2015

2. Revisiting distributed synchronous sgd;chen;International Conference on Learning Representations Workshops Track,2016

3. Anderson acceleration of the Jacobi iterative method: An efficient alternative to Krylov methods for large, sparse linear systems

4. Iterative Methods for Sparse Linear Systems;saad;SIAM,2003

5. On the stationary and non stationary extrapolation iterative method

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2. Analysis of an adaptive safeguarded Newton-Anderson algorithm of depth one with applications to fluid problems;Advances in Computational Science and Engineering;2024

3. Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules;Journal of Cheminformatics;2022-10-17

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