LLR: Learning learning rates by LSTM for training neural networks

Author:

Yu Changyong,Qi XinORCID,Ma Haitao,He Xin,Wang Cuirong,Zhao Yuhai

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference42 articles.

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2. H. Sak, A. Senior, K. Rao, and F. Beaufays, Fast and accurate recurrent neural network acoustic models for speech recognition, arXiv preprint arXiv:1507.06947, 2015.

3. Towards end-to-end speech recognition with recurrent neural networks;Graves,2014

4. W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig, Achieving human parity in conversational speech recognition, arXiv preprint arXiv:1610.05256, 2017.

5. Spatial pyramid pooling in deep convolutional networks for visual recognition;He;In ECCV,2014

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