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Autoencoder
Known as:
Autoassociator
, Diabolo network
, Auto-encoder
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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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Related topics
Related topics
32 relations
Activation function
Artificial neural network
Backpropagation
Conjugate gradient method
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Adversarially Regularized Graph Autoencoder for Graph Embedding
Shirui Pan
,
Ruiqi Hu
,
Guodong Long
,
Jing Jiang
,
Lina Yao
,
Chengqi Zhang
International Joint Conference on Artificial…
2018
Corpus ID: 51608273
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing…
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Highly Cited
2018
Highly Cited
2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
,
R. Barzilay
,
T. Jaakkola
International Conference on Machine Learning
2018
Corpus ID: 3364940
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves…
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Highly Cited
2017
Highly Cited
2017
Grammar Variational Autoencoder
Matt J. Kusner
,
Brooks Paige
,
José Miguel Hernández-Lobato
International Conference on Machine Learning
2017
Corpus ID: 7648414
Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video…
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Highly Cited
2017
Highly Cited
2017
Semantic Autoencoder for Zero-Shot Learning
Elyor Kodirov
,
T. Xiang
,
S. Gong
Computer Vision and Pattern Recognition
2017
Corpus ID: 2633144
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space…
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Highly Cited
2017
Highly Cited
2017
Age Progression/Regression by Conditional Adversarial Autoencoder
Zhifei Zhang
,
Yang Song
,
H. Qi
Computer Vision and Pattern Recognition
2017
Corpus ID: 810708
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…
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Highly Cited
2015
Highly Cited
2015
Variational Autoencoder based Anomaly Detection using Reconstruction Probability
Jinwon An
,
Sungzoon Cho
2015
Corpus ID: 36663713
We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction…
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Highly Cited
2015
Highly Cited
2015
MADE: Masked Autoencoder for Distribution Estimation
M. Germain
,
Karol Gregor
,
Iain Murray
,
H. Larochelle
International Conference on Machine Learning
2015
Corpus ID: 1399080
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We…
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Highly Cited
2013
Highly Cited
2013
Speech enhancement based on deep denoising autoencoder
Xugang Lu
,
Yu Tsao
,
Shigeki Matsuda
,
Chiori Hori
Interspeech
2013
Corpus ID: 13174065
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using…
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Highly Cited
2011
Highly Cited
2011
Autoencoders, Unsupervised Learning, and Deep Architectures
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…
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Highly Cited
2008
Highly Cited
2008
Extracting and composing robust features with denoising autoencoders
Pascal Vincent
,
H. Larochelle
,
Yoshua Bengio
,
Pierre-Antoine Manzagol
International Conference on Machine Learning
2008
Corpus ID: 207168299
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial…
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