Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

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
Expand

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
Autoencoders
An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Review
2019
Review
2019
Review of Deep Learning Algorithms and Architectures
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • figure 4
Is this relevant?
Review
2019
Review
2019
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document… Expand
  • figure 1
  • table 1
  • figure 4
  • table 4
  • table 3
Is this relevant?
Review
2019
Review
2019
A survey on Deep Learning based bearing fault diagnosis
Abstract Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of… Expand
Is this relevant?
Review
2019
Review
2019
One-Class Adversarial Nets for Fraud Detection
Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 2
Is this relevant?
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
  • table 1
  • table 2
Is this relevant?
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
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
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
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
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
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
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
  • figure 1
  • figure 2
  • table 1
  • figure 3
Is this relevant?
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE