Citing TensorFlow

TensorFlow publishes a DOI for the open-source code base using Zenodo.org: 10.5281/zenodo.4724125

TensorFlow's white papers are listed for citation below.

Large-Scale Machine Learning on Heterogeneous Distributed Systems

Access this white paper.

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.

@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},
}

Or in textual form:

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.

TensorFlow: A System for Large-Scale Machine Learning

Access this white paper.

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, general purpose 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.