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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
Wikipedia

Related topics

Related topics

32 relations
Activation functionArtificial neural networkBackpropagationConjugate gradient method
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Grammar Variational Autoencoder
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
Importance Weighted Autoencoders
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
Variational Autoencoder based Anomaly Detection using Reconstruction Probability
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
Adversarial Autoencoders
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
A Hierarchical Neural Autoencoder for Paragraphs and Documents
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
Speech enhancement based on deep denoising autoencoder
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
Autoencoders, Unsupervised Learning, and Deep Architectures
  • 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
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
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
Extracting and composing robust features with denoising autoencoders
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
Autoencoders, Minimum Description Length and Helmholtz Free Energy
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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