Fetching the paper…
Reading the bibliography…
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning.
A practical Bayesian framework for backpropagation networks
David JC MacKay · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Ensemble learning in Bayesian neural networks
David Barber and Christopher M Bishop · 1998
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
Earlier work this paper cites.
Theano: a CPU and GPU math expression compiler
James Bergstra et al · 2010
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
LSTM neural networks for language modeling
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney · 2012
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E others Hinton · 2012
Cited alongside, same era.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
Cited alongside, same era.
Regularization and nonlinearities for neural language models: when are they needed?
Marius Pachitariu and Maneesh Sahani · 2013
Cited alongside, same era.
On fast dropout and its applicability to recurrent networks
Justin Bayer, Christian Osendorfer, Daniela Korhammer, Nutan Chen, Sebastian Urban, and Patrick van der Smagt · 2013
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Later among the works it cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho et al · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Later among the works it cites.
Where to apply dropout in recurrent neural networks for handwriting recognition?
Théodore Bluche, Christopher Kermorvant, and Jérôme Louradour · 2015
Closest in time.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Closest in time.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
Cited alongside, same era.
Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 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.
Dropout improves recurrent neural networks for handwriting recognition
Vu Pham, Theodore Bluche, Christopher Kermorvant, and Jerome Louradour · 2014
Cited alongside, same era.
Bayesian convolutional neural networks with Bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani
Cited in the paper.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani
Cited in the paper.
Jose Miguel Hernandez-Lobato and Ryan Adams · 2015
Closest in time.
Variational dropout and the local reparameterization trick
Diederik Kingma, Tim Salimans, and Max Welling · 2015
Closest in time.
Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
Closest in time.
RnnDrop: A Novel Dropout for RNNs in ASR
Taesup Moon, Heeyoul Choi, Hoshik Lee, and Inchul Song · 2015
Closest in time.