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J-GLOBAL ID:202002235199689821   Reference number:20A2738885

aSNAQ: An adaptive stochastic Nesterov’s accelerated quasi-Newton method for training RNNs

aSNAQ:RNNを訓練するための適応型確率的Nesterovの加速準Newton法
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Volume: 11  Issue:Page: 409-421(J-STAGE)  Publication year: 2020 
JST Material Number: U0219A  ISSN: 2185-4106  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: ENGLISH (EN)
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Artificial intelligence  ,  Neurocomputers 
Reference (30):
  • [1] I. Sutskever, J. Martens, and G.E. Hinton, “Generating text with recurrent neural networks,” Proc. 28th ICML'11, pp. 1017-1024, June 2011.
  • [2] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” Proc. IEEE Conf. CVPR'15, pp. 3156-3164, 2015.
  • [3] A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” Proc. IEEE Conf. CVPR'15, pp. 3128-3137, 2015.
  • [4] D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, September 2014
  • [5] I. Sutskever, O. Vinyals, and Q.V. Le, “Sequence to sequence learning with neural networks,” Advances in Neural Information Processing Systems, pp. 3104-3112, 2014.
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