TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems

TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Jia, Y., Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, & Xiaoqiang Zheng 2015.

Paper abstract bibtex

TensorFlow [1] 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.

@misc{martin_abadi_tensorflow_2015,
	title = {{TensorFlow}: {Large}-{Scale} {Machine} {Learning} on {Heterogeneous} {Systems}},
	url = {https://www.tensorflow.org/},
	abstract = {TensorFlow [1] 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.},
	publisher = {Google Research},
	author = {{Martín Abadi} and {Ashish Agarwal} and {Paul Barham} and {Eugene Brevdo} and {Zhifeng Chen} and {Craig Citro} and {Greg S. Corrado} and {Andy Davis} and {Jeffrey Dean} and {Matthieu Devin} and {Sanjay Ghemawat} and {Ian Goodfellow} and {Andrew Harp} and {Geoffrey Irving} and {Michael Isard} and Jia, Yangqing and {Rafal Jozefowicz} and {Lukasz Kaiser} and {Manjunath Kudlur} and {Josh Levenberg} and {Dandelion Mané} and {Rajat Monga} and {Sherry Moore} and {Derek Murray} and {Chris Olah} and {Mike Schuster} and {Jonathon Shlens} and {Benoit Steiner} and {Ilya Sutskever} and {Kunal Talwar} and {Paul Tucker} and {Vincent Vanhoucke} and {Vijay Vasudevan} and {Fernanda Viégas} and {Oriol Vinyals} and {Pete Warden} and {Martin Wattenberg} and {Martin Wicke} and {Yuan Yu} and {Xiaoqiang Zheng}},
	year = {2015},
}

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