2016 TensorFlowLargeScaleMachineLear

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Subject Headings: TensorFlow.

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Abstract

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2016 TensorFlowLargeScaleMachineLearJeffrey Dean
Sanjay Ghemawat
Ashish Agarwal
Mike Schuster
Ilya Sutskever
Oriol Vinyals
Martin Wattenberg
Yangqing Jia
Vincent Vanhoucke
Ian Goodfellow
Martín Abadi
Paul Barham
Jianmin Chen
Zhifeng Chen
Andy Davis
Matthieu Devin
Geoffrey Irving
Michael Isard
Manjunath Kudlur
Josh Levenberg
Rajat Monga
Sherry Moore
Derek G. Murray
Benoit Steiner
Paul Tucker
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zheng
Lukasz Kaiser
Eugene Brevdo
Craig Citro
Greg S. Corrado
Andrew Harp
Rafal Jozefowicz
Dan Mane
Derek Murray
Chris Olah
Jonathon Shlens
Kunal Talwar
Fernanda Viegas
TensorFlow - Large-Scale Machine Learning on Heterogeneous Distributed Systems2016