Corpus ID: 388785

Learning What Data to Learn

@article{Fan2017LearningWD,
  title={Learning What Data to Learn},
  author={Yang Fan and Fei Tian and Tao Qin and Jiang Bian and Tie-Yan Liu},
  journal={ArXiv},
  year={2017},
  volume={abs/1702.08635}
}
  • Yang Fan, Fei Tian, +2 authors Tie-Yan Liu
  • Published 2017
  • Computer Science, Mathematics
  • ArXiv
  • Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter} (\textbf{NDF}), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a… CONTINUE READING

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