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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… 
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Papers overview

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Highly Cited
2018
Highly Cited
2018
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.  Most existing… 
Highly Cited
2018
Highly Cited
2018
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves… 
Highly Cited
2017
Highly Cited
2017
Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video… 
Highly Cited
2017
Highly Cited
2017
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space… 
Highly Cited
2017
Highly Cited
2017
If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face… 
Highly Cited
2015
Highly Cited
2015
We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction… 
Highly Cited
2015
Highly Cited
2015
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We… 
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… 
Highly Cited
2011
Highly Cited
2011
  • P. Baldi
  • ICML Unsupervised and Transfer Learning
  • 2011
  • Corpus ID: 10921035
Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In… 
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…