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-# TensorFlow White Papers
-
-This document identifies white papers about TensorFlow.
-
-## Large-Scale Machine Learning on Heterogeneous Distributed Systems
-
-[Access this white paper.](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
-
-**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.
-
-
-### In BibTeX format
-
-If you use TensorFlow in your research and would like to cite the TensorFlow
-system, we suggest you cite this whitepaper.
-
-<pre>
-@misc{tensorflow2015-whitepaper,
-title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
-url={https://www.tensorflow.org/},
-note={Software available from tensorflow.org},
-author={
-    Mart\'{\i}n~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
-    Yangqing Jia and
-    Rafal~Jozefowicz and
-    Lukasz~Kaiser and
-    Manjunath~Kudlur and
-    Josh~Levenberg and
-    Dandelion~Man\'{e} 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~Vi\'{e}gas and
-    Oriol~Vinyals and
-    Pete~Warden and
-    Martin~Wattenberg and
-    Martin~Wicke and
-    Yuan~Yu and
-    Xiaoqiang~Zheng},
-  year={2015},
-}
-</pre>
-
-Or in textual form:
-
-<pre>
-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, Rafal Jozefowicz, Yangqing Jia,
-Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster,
-Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, 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, and Xiaoqiang Zheng.
-TensorFlow: Large-scale machine learning on heterogeneous systems,
-2015. Software available from tensorflow.org.
-</pre>
-
-
-
-## TensorFlow: A System for Large-Scale Machine Learning
-
-[Access this white paper.](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf)
-
-**Abstract:** TensorFlow is a machine learning system that operates at
-large scale and in heterogeneous environments. TensorFlow
-uses dataflow graphs to represent computation,
-shared state, and the operations that mutate that state. It
-maps the nodes of a dataflow graph across many machines
-in a cluster, and within a machine across multiple computational
-devices, including multicore CPUs, generalpurpose
-GPUs, and custom-designed ASICs known as
-Tensor Processing Units (TPUs). This architecture gives
-flexibility to the application developer: whereas in previous
-“parameter server” designs the management of shared
-state is built into the system, TensorFlow enables developers
-to experiment with novel optimizations and training algorithms.
-TensorFlow supports a variety of applications,
-with a focus on training and inference on deep neural networks.
-Several Google services use TensorFlow in production,
-we have released it as an open-source project, and
-it has become widely used for machine learning research.
-In this paper, we describe the TensorFlow dataflow model
-and demonstrate the compelling performance that TensorFlow
-achieves for several real-world applications.
-