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Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning.
Learning long term dependencies is hard
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Exploring Deep Learning Methods for discovering features in speech signals
N. Jaitly · 2014
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End-to-end continuous speech recognition using attention-based recurrent nn: First results
Deep captioning with multimodal recurrent neural networks (m-rnn)
J. Mao, W. Xu, Y. Yang, J. Wang, Z. H. Huang, and A. Yuille · 2015
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Unifying visual-semantic embeddings with multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. Zemel · 2015
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Deep visual-semantic alignments for generating image descriptions
A. Karpathy and F.-F. Li · 2015
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From captions to visual concepts and back
H. Fang, S. Gupta, F. Iandola, R. K. Srivastava, L. Deng, P. Dollar, J. Gao, X. He, M. Mitchell, J. C. Platt, C. L. Zitnick, and G. Zweig · 2015
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R. Vedantam, C. L. Zitnick, and D. Parikh · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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Long-term recurrent convolutional networks for visual recognition and description
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Improving multi-step prediction of learned time series models
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Microsoft coco captioning challenge
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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