Fetching the paper…
Reading the bibliography…
Data noising is an effective technique for regularizing neural network models.
Class-based n-gram models of natural language
Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai · 1992
Earlier work this paper cites.
An empirical study of smoothing techniques for language modeling
Stanley F Chen and Joshua Goodman · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based Learning Applied to Document Recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Large-vocabulary speech recognition under adverse acoustic environments
Li Deng, Alex Acero, Mike Plumpe, and Xuedong Huang · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin · 2003
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Statistical language models based on neural networks
Tomáš Mikolov · 2012
Earlier work this paper cites.
Feature noising for log-linear structured prediction
Sida I Wang, Mengqiu Wang, Stefan Wager, Percy Liang, and Christopher D Manning · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, et al · 2014
Cited alongside, same era.
Dropout improves recurrent neural networks for handwriting recognition
Vu Pham, Théodore Bluche, Christopher Kermorvant, and Jérôme Louradour · 2014
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
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Cited alongside, same era.
Altitude training: Strong bounds for single-layer dropout
S. Wager, W. Fithian, S. I. Wang, and P. Liang · 2014
Cited alongside, same era.
Regularizing rnns by stabilizing activations
David Krueger and Roland Memisevic · 2015
Later among the works it cites.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher · 2015
Later among the works it cites.
A simple way to initialize recurrent networks of rectified linear units
Quoc V Le, Navdeep Jaitly, and Geoffrey E Hinton · 2015
Later among the works it cites.
Stanford neural machine translation systems for spoken language domains
Minh-Thang Luong and Christopher D Manning · 2015
Later among the works it cites.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
Cited alongside, same era.
Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2015
Cited alongside, same era.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Cited alongside, same era.
Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
Cited alongside, same era.
Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal · 2015
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III · 2015
Cited alongside, same era.
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Later among the works it cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Later among the works it cites.
Recurrent dropout without memory loss
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth · 2016
Later among the works it cites.
Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
Later among the works it cites.
Data augmentation via levy processes
Stefan Wager, William Fithian, and Percy Liang · 2016
Later among the works it cites.
Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2016
Later among the works it cites.