TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Preprint English OPEN
Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael; Jia, Yangqing; Jozefowicz, Rafal; Kaiser, Lukasz; Kudlur, Manjunath; Levenberg, Josh; Mane, Dan; Monga, Rajat; Moore, Sherry; Murray, Derek; Olah, Chris; Schuster, Mike; Shlens, Jonathon; Steiner, Benoit; Sutskever, Ilya; Talwar, Kunal; ... view all 40 authors
(2016)
  • Subject: Computer Science - Distributed, Parallel, and Cluster Computing | Computer Science - Learning

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 ... View more
  • References (42)
    42 references, page 1 of 5

    [1] Mart´ın 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, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane´, 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 Vie´gas, 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.

    [2] Anelia Angelova, Alex Krizhevsky, and Vincent Vanhoucke. Pedestrian detection with a large-field-of-view deep network. In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages 704-711. IEEE, 2015. CalTech PDF.

    [3] Arvind and David E. Culler. of computer science vol. 1, Annual review 1986. chapter

    [4] Arvind and Rishiyur S. Nikhil. Executing a program on the MIT tagged-token dataflow architecture. IEEE Trans. Comput., 39(3):300-318, 1990. dl.acm.org/citation.cfm?id=78583.

    [5] Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755, 2014. arxiv.org/abs/1412.7755.

    [6] Franc¸oise Beaufays. The neural behind Google Voice transcription, googleresearch.blogspot.com/2015/08/the-neuralnetworks-behind-google-voice.html.

    networks 2015.

    [7] James Bergstra, Olivier Breuleux, Fre´de´ric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. Theano: A CPU and GPU math expression compiler. In Proceedings of the Python for scientific computing conference (SciPy), volume 4, page 3. Austin, TX, 2010. UMontreal PDF.

    [8] Craig Chambers, Ashish Raniwala, Frances Perry, Stephen Adams, Robert R Henry, Robert Bradshaw, and Nathan Weizenbaum. FlumeJava: easy, efficient data-parallel pipelines. In ACM Sigplan Notices, volume 45, pages 363-375. ACM, 2010. research.google.com/pubs/archive/35650.pdf.

    [9] Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. cuDNN: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759, 2014. arxiv.org/abs/1410.0759.

  • Metrics
Share - Bookmark