TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

Heng Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie. TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017. pages 1763-1771, ACM, 2017. [doi]

@inproceedings{ChengHHIMPRSSST17,
  title = {TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks},
  author = {Heng Tze Cheng and Zakaria Haque and Lichan Hong and Mustafa Ispir and Clemens Mewald and Illia Polosukhin and Georgios Roumpos and D. Sculley and Jamie Smith and David Soergel and Yuan Tang and Philipp Tucker and Martin Wicke and Cassandra Xia and Jianwei Xie},
  year = {2017},
  doi = {10.1145/3097983.3098171},
  url = {http://doi.acm.org/10.1145/3097983.3098171},
  researchr = {https://researchr.org/publication/ChengHHIMPRSSST17},
  cites = {0},
  citedby = {0},
  pages = {1763-1771},
  booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017},
  publisher = {ACM},
  isbn = {978-1-4503-4887-4},
}