vqSGD: Vector Quantized Stochastic Gradient Descent-Reference-Cited by-同舟云学术

vqSGD: Vector Quantized Stochastic Gradient Descent

Published:2022-07 Issue:7 Volume:68 Page:4573-4587
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Gandikota Venkata¹^ORCID,Kane Daniel²,Maity Raj Kumar³^ORCID,Mazumdar Arya⁴^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA

2. Departments of Computer Science and Mathematics, UC San Diego, La Jolla, CA, USA

3. College of Information and Computer Sciences, UMass Amherst, Amherst, MA, USA

4. Halicioglu Data Science Institute, UC San Diego, La Jolla, CA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/9798707/9740125-aam.pdf

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4. Information-theoretic lower bounds for distributed statistical estimation with communication constraints;zhang;Proc Adv Neural Inf Process Syst,2013

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