Elastic Consistency

Author:

Alistarh Dan1,Markov Ilia1,Nadiradze Giorgi1

Affiliation:

1. IST Austria, Klosterneuburg, Austria

Abstract

Machine learning models can match or surpass humans on specialized tasks such as image classification [20, 14], speech recognition [37], or complex games [39]. One key tool behind this progress has been a family of optimization methods which fall under the umbrella term of stochastic gradient descent (SGD) [35], which are by and large the method of choice for training large-scale machine learning models.

Publisher

Association for Computing Machinery (ACM)

Subject

General Materials Science

Reference51 articles.

1. Martín Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , : A system for large-scale machine learning . In OSDI , volume 16 , pages 265 -- 283 , 2016 . Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. Tensorflow: A system for large-scale machine learning. In OSDI, volume 16, pages 265--283, 2016.

2. Sparse Communication for Distributed Gradient Descent

3. The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory

4. Dan Alistarh , Demjan Grubic , Jerry Li , Ryota Tomioka , and Milan Vojnovic . Qsgd : Communicationefficient sgd via gradient quantization and encoding . In NIPS , pages 1709 -- 1720 , 2017 . Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. Qsgd: Communicationefficient sgd via gradient quantization and encoding. In NIPS, pages 1709--1720, 2017.

5. Dan Alistarh , Torsten Hoefler , Mikael Johansson , Nikola Konstantinov , Sarit Khirirat , and Cédric Renggli . The convergence of sparsified gradient methods . In NIPS , pages 5977 -- 5987 , 2018 . Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli. The convergence of sparsified gradient methods. In NIPS, pages 5977--5987, 2018.

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