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Autoencoder

Known as: Autoassociator, Diabolo network, Auto-encoder 
An autoencoder, autoassociator or Diabolo network is an artificial neural network used for unsupervised learning of efficient codings.The aim of an… Expand
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Papers overview

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Highly Cited
2017
Highly Cited
2017
Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video… Expand
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Highly Cited
2016
Highly Cited
2016
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative… Expand
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Highly Cited
2015
Highly Cited
2015
We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction… Expand
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Highly Cited
2015
Highly Cited
2015
In this paper, we propose the “adversarial autoencoder” (AAE), which is a probabilistic autoencoder that uses the recently… Expand
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Highly Cited
2015
Highly Cited
2015
Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent… Expand
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Highly Cited
2013
Highly Cited
2013
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using… Expand
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Highly Cited
2012
Highly Cited
2012
  • P. Baldi
  • ICML Unsupervised and Transfer Learning
  • 2012
  • Corpus ID: 10921035
Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In… Expand
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Highly Cited
2010
Highly Cited
2010
We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained… Expand
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Highly Cited
2008
Highly Cited
2008
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial… Expand
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Highly Cited
1993
Highly Cited
1993
An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of… Expand
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