Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent-Reference-Cited by-同舟云学术

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent

Published:2017-06-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th Annual International Symposium on Computer Architecture
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Author:

De Sa Christopher¹,Feldman Matthew¹,Ré Christopher¹,Olukotun Kunle¹

Affiliation:

1. Departments of Electrical Engineering and Computer Science, Stanford University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3079856.3080248

Reference57 articles.

1. BOOST C++ Libraries. http://www.boost.org. BOOST C++ Libraries. http://www.boost.org.

2. Alekh Agarwal Olivier Chapelle Miroslav Dudík and John Langford. 2011. A Reliable Effective Terascale Linear Learning System. CoRR abs/1110.4198 (2011). Alekh Agarwal Olivier Chapelle Miroslav Dudík and John Langford. 2011. A Reliable Effective Terascale Linear Learning System. CoRR abs/1110.4198 (2011).

3. Validity of the single processor approach to achieving large scale computing capabilities

4. Large-Scale Machine Learning with Stochastic Gradient Descent

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