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Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies.
“The design for the wall street journal-based csr corpus,”
Douglas B Paul and Janet M Baker, · 1992
Earlier work this paper cites.
“Building a large annotated corpus of english: The penn treebank,”
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini, · 1993
Earlier work this paper cites.
“Long short-term memory,”
Sepp Hochreiter and Jürgen Schmidhuber, · 1997
Earlier work this paper cites.
“Bidirectional recurrent neural networks,”
Mike Schuster and Kuldip K Paliwal, · 1997
Earlier work this paper cites.
“Learning precise timing with lstm recurrent networks,”
Felix A Gers, Nicol N Schraudolph, and Jürgen Schmidhuber, · 2003
Earlier work this paper cites.
“Understanding the difficulty of training deep feedforward neural networks,”
Xavier Glorot and Yoshua Bengio, · 2010
Earlier work this paper cites.
“Statistical language models based on neural networks,”
Tomáš Mikolov, · 2012
Earlier work this paper cites.
“Efficient backprop,”
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller, · 2012
Earlier work this paper cites.
“On the difficulty of training recurrent neural networks,”
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio, · 2012
Cited alongside, same era.
“Theano: new features and speed improvements,” Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012
Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, and Yoshua Bengio, · 2012
Cited alongside, same era.
“Speech recognition with deep recurrent neural networks,”
Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton, · 2013
Cited alongside, same era.
“How to construct deep recurrent neural networks,”
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio, · 2013
Cited alongside, same era.
“Hybrid speech recognition with deep bidirectional lstm,”
Alan Graves, Navdeep Jaitly, and Abdel-rahman Mohamed, · 2013
Cited alongside, same era.
“Dropout: A simple way to prevent neural networks from overfitting,”
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, · 2014
Later among the works it cites.
“Recurrent neural network regularization,”
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals, · 2014
Later among the works it cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
Closest in time.
“Scaling recurrent neural network language models,”
Will Williams, Niranjani Prasad, David Mrva, Tom Ash, and Tony Robinson, · 2015
Closest in time.
Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, and Koray Kavukcuoglu, · 2015
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“Sequence to sequence learning with neural networks,”
Ilya Sutskever, Oriol Vinyals, and Quoc Le, · 2014
Cited alongside, same era.
“Neural machine translation by jointly learning to align and translate,”
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, · 2014
Cited alongside, same era.
“Deepspeech: Scaling up end-to-end speech recognition,”
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al., · 2014
Cited alongside, same era.
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“ImageNet Large Scale Visual Recognition Challenge,”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei, · 2015
Closest in time.
“Blocks and Fuel: Frameworks for deep learning,”
B. van Merriënboer, D. Bahdanau, V. Dumoulin, D. Serdyuk, D. Warde-Farley, J. Chorowski, and Y. Bengio, · 2015
Closest in time.