Auto-encoders: reconstruction versus compression

Learning sets of filters using back-propagation

David C. Plaut and Geoffrey Hinton · 1987

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.

Training with noise is equivalent to Tikhonov regularization

Christopher M. Bishop · 1995

Earlier work this paper cites.

Efficient backprop

Yann LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller · 1996

Earlier work this paper cites.

Noise injection: Theoretical prospects

Yves Grandvalet, Stéphane Canu, and Stéphane Boucheron · 1997

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Pattern recognition and machine learning

Christopher M. Bishop · 2006

Cited alongside, same era.

Reducing the dimensionality of data with neural networks

Geoffrey E. Hinton and Ruslan R. Salakhutdinov · 2006

Cited alongside, same era.

The minimum description length principle

Peter D. Grünwald · 2007

Cited alongside, same era.

Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion

Original

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010

Cited alongside, same era.

Practical variational inference for neural networks

Alex Graves · 2011

Cited alongside, same era.

Contractive auto-encoders: Explicit invariance during feature extraction

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011

Later among the works it cites.

Layer-wise training of deep generative models

Original

Ludovic Arnold and Yann Ollivier · 2012

Later among the works it cites.

Stochastic gradient VB and the variational auto-encoder

Original

Diederik P. Kingma and Max Welling · 2013

Later among the works it cites.

Riemannian metrics for neural networks I: feedforward networks

Original

Yann Ollivier · 2013

Later among the works it cites.