Auto-encodeur variationnel (French Wikipedia)

Analysis of information sources in references of the Wikipedia article "Auto-encodeur variationnel" in French language version.

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6doi.org

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5semanticscholar.org

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4arxiv.org

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3nih.gov

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2issn.org

57^th place

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1wiley.com

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1harvard.edu

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1sciencemag.org

857^th place

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1evjang.com

low place

1thecvf.com

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1aaai.org

9,352^nd place

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1mit.edu

415^th place

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1lilianweng.github.io

low place

1ieee.org

652^nd place

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aaai.org

ojs.aaai.org

(en) Xu, Sun, Deng et Tan, « Variational Autoencoder for Semi-Supervised Text Classification », Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, n^o 1,‎ 12 février 2017 (lire en ligne)

arxiv.org

(en) Diederik P. Kingma et Max Welling, « Auto-Encoding Variational Bayes », 1^er mai 2014..
Wei-Ning Hsu, Yu Zhang et James Glass, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), décembre 2017, 16–23 p. (ISBN 978-1-5090-4788-8, DOI 10.1109/ASRU.2017.8268911, arXiv 1707.06265, S2CID 22681625), « Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation »
Kingma et Welling, « An Introduction to Variational Autoencoders », Foundations and Trends in Machine Learning, vol. 12, n^o 4,‎ 2019, p. 307–392 (ISSN 1935-8237, DOI 10.1561/2200000056, arXiv 1906.02691, S2CID 174802445)
Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

doi.org

dx.doi.org

(en) Kramer, « Nonlinear principal component analysis using autoassociative neural networks », AIChE Journal, vol. 37, n^o 2,‎ 1991, p. 233–243 (DOI 10.1002/aic.690370209, lire en ligne)
(en) Hinton et Salakhutdinov, « Reducing the Dimensionality of Data with Neural Networks », Science, vol. 313, n^o 5786,‎ 28 juillet 2006, p. 504–507 (PMID 16873662, DOI 10.1126/science.1127647, Bibcode 2006Sci...313..504H, S2CID 1658773, lire en ligne).
Wei-Ning Hsu, Yu Zhang et James Glass, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), décembre 2017, 16–23 p. (ISBN 978-1-5090-4788-8, DOI 10.1109/ASRU.2017.8268911, arXiv 1707.06265, S2CID 22681625), « Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation »
Kameoka, Li, Inoue et Makino, « Supervised Determined Source Separation with Multichannel Variational Autoencoder », Neural Computation, vol. 31, n^o 9,‎ 1^er septembre 2019, p. 1891–1914 (PMID 31335290, DOI 10.1162/neco_a_01217, S2CID 198168155, lire en ligne)
Kingma et Welling, « An Introduction to Variational Autoencoders », Foundations and Trends in Machine Learning, vol. 12, n^o 4,‎ 2019, p. 307–392 (ISSN 1935-8237, DOI 10.1561/2200000056, arXiv 1906.02691, S2CID 174802445)
Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

evjang.com

blog.evjang.com

(en) « A Beginner's Guide to Variational Methods: Mean-Field Approximation », Eric Jang, 8 juillet 2016

harvard.edu

ui.adsabs.harvard.edu

(en) Hinton et Salakhutdinov, « Reducing the Dimensionality of Data with Neural Networks », Science, vol. 313, n^o 5786,‎ 28 juillet 2006, p. 504–507 (PMID 16873662, DOI 10.1126/science.1127647, Bibcode 2006Sci...313..504H, S2CID 1658773, lire en ligne).

ieee.org

ieeexplore.ieee.org

Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

issn.org

portal.issn.org

Kingma et Welling, « An Introduction to Variational Autoencoders », Foundations and Trends in Machine Learning, vol. 12, n^o 4,‎ 2019, p. 307–392 (ISSN 1935-8237, DOI 10.1561/2200000056, arXiv 1906.02691, S2CID 174802445)
Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

lilianweng.github.io

(en) « From Autoencoder to Beta-VAE », Lil'Log, 12 août 2018

mit.edu

direct.mit.edu

Kameoka, Li, Inoue et Makino, « Supervised Determined Source Separation with Multichannel Variational Autoencoder », Neural Computation, vol. 31, n^o 9,‎ 1^er septembre 2019, p. 1891–1914 (PMID 31335290, DOI 10.1162/neco_a_01217, S2CID 198168155, lire en ligne)

nih.gov

ncbi.nlm.nih.gov

(en) Hinton et Salakhutdinov, « Reducing the Dimensionality of Data with Neural Networks », Science, vol. 313, n^o 5786,‎ 28 juillet 2006, p. 504–507 (PMID 16873662, DOI 10.1126/science.1127647, Bibcode 2006Sci...313..504H, S2CID 1658773, lire en ligne).
Kameoka, Li, Inoue et Makino, « Supervised Determined Source Separation with Multichannel Variational Autoencoder », Neural Computation, vol. 31, n^o 9,‎ 1^er septembre 2019, p. 1891–1914 (PMID 31335290, DOI 10.1162/neco_a_01217, S2CID 198168155, lire en ligne)
Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

sciencemag.org

science.sciencemag.org

(en) Hinton et Salakhutdinov, « Reducing the Dimensionality of Data with Neural Networks », Science, vol. 313, n^o 5786,‎ 28 juillet 2006, p. 504–507 (PMID 16873662, DOI 10.1126/science.1127647, Bibcode 2006Sci...313..504H, S2CID 1658773, lire en ligne).

semanticscholar.org

api.semanticscholar.org

(en) Hinton et Salakhutdinov, « Reducing the Dimensionality of Data with Neural Networks », Science, vol. 313, n^o 5786,‎ 28 juillet 2006, p. 504–507 (PMID 16873662, DOI 10.1126/science.1127647, Bibcode 2006Sci...313..504H, S2CID 1658773, lire en ligne).
Wei-Ning Hsu, Yu Zhang et James Glass, 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), décembre 2017, 16–23 p. (ISBN 978-1-5090-4788-8, DOI 10.1109/ASRU.2017.8268911, arXiv 1707.06265, S2CID 22681625), « Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation »
Kameoka, Li, Inoue et Makino, « Supervised Determined Source Separation with Multichannel Variational Autoencoder », Neural Computation, vol. 31, n^o 9,‎ 1^er septembre 2019, p. 1891–1914 (PMID 31335290, DOI 10.1162/neco_a_01217, S2CID 198168155, lire en ligne)
Kingma et Welling, « An Introduction to Variational Autoencoders », Foundations and Trends in Machine Learning, vol. 12, n^o 4,‎ 2019, p. 307–392 (ISSN 1935-8237, DOI 10.1561/2200000056, arXiv 1906.02691, S2CID 174802445)
Bengio, Courville et Vincent, « Representation Learning: A Review and New Perspectives », IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, n^o 8,‎ 2013, p. 1798–1828 (ISSN 1939-3539, PMID 23787338, DOI 10.1109/TPAMI.2013.50, arXiv 1206.5538, S2CID 393948, lire en ligne)

thecvf.com

openaccess.thecvf.com

M. Ehsan Abbasnejad, Anthony Dick et Anton van den Hengel, Infinite Variational Autoencoder for Semi-Supervised Learning, 2017, 5888–5897 p. (lire en ligne)

wiley.com

aiche.onlinelibrary.wiley.com

(en) Kramer, « Nonlinear principal component analysis using autoassociative neural networks », AIChE Journal, vol. 37, n^o 2,‎ 1991, p. 233–243 (DOI 10.1002/aic.690370209, lire en ligne)