Characterize and Compare the Performance of Deep Learning Optimizers in Recurrent Neural Network Architectures-Reference-Cited by-同舟云学术

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Characterize and Compare the Performance of Deep Learning Optimizers in Recurrent Neural Network Architectures

Published:2024-07-02 Issue: Volume:12 Page:39-44
ISSN:
Container-title:2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

Zaeed Mohammad¹,Islam Tanzima Z.¹,Indict Vladimir²

Affiliation:

1. Texas State University

2. University of Novi Sad

Funder

U.S. Department of Energy

Office of Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10633276/10633246/10633432.pdf?arnumber=10633432

Reference17 articles.

1. Omniperf deployment guide on amd accelerator cloud (aac);I. Advanced Micro Devices

2. Adaptive subgradient methods for online learning and stochastic optimization;Duchi;Journal of machine learning research,2011

3. Adam: A method for stochastic optimization;Kingma;arXiv preprint,2014

4. Amd instinct mi100 instruction set architecture,2020

5. A Machine Learning Framework for Performance Coverage Analysis of Proxy Applications

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