Fetching the paper…
Reading the bibliography…
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.
The problem of learning long-term dependencies in recurrent networks
Bengio, Y., Frasconi, P., and Simard, P · 1993
Earlier work this paper cites.
Timit acoustic-phonetic continuous speech corpus ldc93s1
Garofolo, J., Lamel, L., Fisher, W., Fiscus, J., Pallett, D., Dahlgren, N., and Zue, V · 1993
Earlier work this paper cites.
Voicebox: Speech processing toolbox for matlab
Brookes, M. et al · 1997
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs
Rix, A. W., Beerends, J. G., Hollier, M. P., and Hekstra, A. P · 2001
Earlier work this paper cites.
Is there a small skew cayley transform with zero diagonal?
Kahan, W · 2006
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
Earlier work this paper cites.
An algorithm for intelligibility prediction of time–frequency weighted noisy speech
Taal, C. H., Hendriks, R. C., Heusdens, R., and Jensen, J · 2011
Earlier work this paper cites.
Notes on optimization on stiefel manifolds
Tagare, H. D · 2011
Cited alongside, same era.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
Cited alongside, same era.
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
Cited alongside, same era.
A feasible method for optimization with orthogonality constraints
Wen, Z. and Yin, W · 2013
Cited alongside, same era.
On the properties of neural machine translation: Encoder-decoder approaches, 2014
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y · 2014
Cited alongside, same era.
Tunable efficient unitary neural networks (eunn) and their application to rnn, 2016
Jing, L., Shen, Y., Dubček, T., Peurifoy, J., Skirlo, S., Tegmark, M., and Soljačić, M · 2016
Later among the works it cites.
Full-capacity unitary recurrent neural networks
Wisdom, S., Powers, T., Hershey, J., Roux, J. L., and Atlas, L · 2016
Later among the works it cites.
Recurrent orthogonal networks and long-memory tasks
Henaff, M., Szlam, A., and LeCun, Y · 2017
Closest in time.
Learning unitary operators with help from u(n)
Hyland, S. L. and Gunnar, R · 2017
Closest in time.
Gated orthogonal recurrent units: On learning to forget
Jing, L., Gülçehre, C., Peurifoy, J., Shen, Y., Tegmark, M., Soljačić, M., and Bengio, Y · 2017
Closest in time.
Efficient orthogonal parameterisation of recurrent neural networks using householder reflections
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kingma, D. and Ba, J · 2014
Cited alongside, same era.
A simple way to initialize recurrent networks of rectified linear units, 2015
Le, Q. V., Jaitly, N., and Hinton, G. E · 2015
Cited alongside, same era.
Unitary evolution recurrent neural networks
Arjovsky, M., Shah, A., and Bengio, Y · 2016
Cited alongside, same era.
The mnist database
LeCun, Y., Cortes, C., and Burges, C. J. C
Cited in the paper.
Mhammedi, Z., Hellicar, A., Rahman, A., and Bailey, J · 2017
Closest in time.
On orthogonality and learning recurrent networks with long term dependencies, 2017
Vorontsov, E., Trabelsi, C., Kadoury, S., and Pal, C · 2017
Closest in time.