Corpus ID: 5707386

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

@article{Abadi2016TensorFlowLM,
  title={TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems},
  author={M. Abadi and A. Agarwal and P. Barham and E. Brevdo and Z. Chen and Craig Citro and G. S. Corrado and Andy Davis and J. Dean and M. Devin and Sanjay Ghemawat and Ian J. Goodfellow and A. Harp and Geoffrey Irving and M. Isard and Y. Jia and R. J{\'o}zefowicz and L. Kaiser and M. Kudlur and Josh Levenberg and Dan Man{\'e} and Rajat Monga and Sherry Moore and D. Murray and Chris Olah and Mike Schuster and Jonathon Shlens and B. Steiner and Ilya Sutskever and Kunal Talwar and P. Tucker and V. Vanhoucke and V. Vasudevan and F. Vi{\'e}gas and Oriol Vinyals and Pete Warden and M. Wattenberg and Martin Wicke and Y. Yu and X. Zheng},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.04467}
}

M. Abadi, A. Agarwal, +37 authors X. Zheng

Published 2016

Computer Science

ArXiv

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… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed