Deep learning (Hungarian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Deep learning" in Hungarian language version.

refsWebsite

Global rank Hungarian rank

14doi.org

2^nd place

8^th place

5nih.gov

4^th place

11^th place

3web.archive.org

1^st place

3arxiv.org

69^th place

778^th place

3google.hu

8,839^th place

51^st place

2nips.cc

low place

1hal.science

low place

1toronto.edu

low place

1techcrunch.com

187^th place

597^th place

1mit.edu

415^th place

753^rd place

1sanghv.com

low place

1books.google.com

3^rd place

28^th place

1researchgate.net

120^th place

344^th place

1springer.com

274^th place

438^th place

1worldcat.org

5^th place

22^nd place

1cmu.edu

1,564^th place

4,669^th place

1archive.org

6^th place

14^th place

1microsoft.com

153^rd place

319^th place

1utl.pt

low place

archive.org (Global: 6^th place; Hungarian: 14^th place)

(1991) „Approximation Capabilities of Multilayer Feedforward Networks”. Neural Networks 4 (2), 251–257. o. DOI:10.1016/0893-6080(91)90009-t.

arxiv.org (Global: 69^th place; Hungarian: 778^th place)

(2013) „Representation Learning: A Review and New Perspectives”. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8), 1798–1828. o. DOI:10.1109/tpami.2013.50. PMID 23787338. arXiv 1206.5538.
Schmidhuber, J. (2015). „Deep Learning in Neural Networks: An Overview”. Neural Networks 61, 85–117. o. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637. arXiv 1404.7828.
(2017) „Neural network with unbounded activation functions is universal approximator”. Applied and Computational Harmonic Analysis 43 (2), 233–268. o. DOI:10.1016/j.acha.2015.12.005. arXiv 1505.03654.

books.google.com (Global: 3^rd place; Hungarian: 28^th place)

Shigeki, Sugiyama. Human Behavior and Another Kind in Consciousness: Emerging Research and Opportunities: Emerging Research and Opportunities (angol nyelven). IGI Global (2019. április 12.). ISBN 978-1-5225-8218-2

cmu.edu (Global: 1,564^th place; Hungarian: 4,669^th place)

deeplearning.cs.cmu.edu

(1989) „Approximations by superpositions of sigmoidal functions”. Mathematics of Control, Signals, and Systems 2 (4), 303–314. o. DOI:10.1007/bf02551274.

doi.org (Global: 2^nd place; Hungarian: 8^th place)

dx.doi.org

(2015) „Deep Learning”. Nature 521 (7553), 436–444. o. DOI:10.1038/nature14539. PMID 26017442.
Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3642–3649. o.. DOI: 10.1109/cvpr.2012.6248110 (2012). ISBN 978-1-4673-1228-8
Bengio, Yoshua (2009). „Learning Deep Architectures for AI”. Foundations and Trends in Machine Learning 2 (1), 1–127. o. DOI:10.1561/2200000006. (Hozzáférés: 2015. szeptember 3.)
(2013) „Representation Learning: A Review and New Perspectives”. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8), 1798–1828. o. DOI:10.1109/tpami.2013.50. PMID 23787338. arXiv 1206.5538.
Schmidhuber, J. (2015). „Deep Learning in Neural Networks: An Overview”. Neural Networks 61, 85–117. o. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637. arXiv 1404.7828.
(2009) „Deep belief networks”. Scholarpedia 4 (5), 5947. o. DOI:10.4249/scholarpedia.5947.
Multi-Valued and Universal Binary Neurons. Science & Business Media. DOI: 10.1007/978-1-4757-3115-6 (2000. október 24.). ISBN 978-0-7923-7824-2
Fradkov, Alexander L. (2020. január 1.). „Early History of Machine Learning”. IFAC-PapersOnLine 53 (2), 1385–1390. o. DOI:10.1016/j.ifacol.2020.12.1888. ISSN 2405-8963.
(1989) „Approximations by superpositions of sigmoidal functions”. Mathematics of Control, Signals, and Systems 2 (4), 303–314. o. DOI:10.1007/bf02551274.
(1991) „Approximation Capabilities of Multilayer Feedforward Networks”. Neural Networks 4 (2), 251–257. o. DOI:10.1016/0893-6080(91)90009-t.
(2017) „Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback”. Nature Communications 8 (1), 138. o. DOI:10.1038/s41467-017-00181-8. PMID 28743932. PMC 5527101.
(2014) „Deep Learning: Methods and Applications”. Foundations and Trends in Signal Processing 7 (3–4), 1–199. o. DOI:10.1561/2000000039. (Hozzáférés: 2014. október 18.)
(1969) „Visual feature extraction by a multilayered network of analog threshold elements”. IEEE Transactions on Systems Science and Cybernetics 5 (4), 322–333. o. DOI:10.1109/TSSC.1969.300225.
(2017) „Neural network with unbounded activation functions is universal approximator”. Applied and Computational Harmonic Analysis 43 (2), 233–268. o. DOI:10.1016/j.acha.2015.12.005. arXiv 1505.03654.

google.hu (Global: 8,839^th place; Hungarian: 51^st place)

books.google.hu

Haykin, Simon S.. Neural Networks: A Comprehensive Foundation. Prentice Hall (1999). ISBN 978-0-13-273350-2
Hassoun, Mohamad H.. Fundamentals of Artificial Neural Networks. MIT Press, 48. o. (1995). ISBN 978-0-262-08239-6
Murphy, Kevin P.. Machine Learning: A Probabilistic Perspective. MIT Press (2012. augusztus 24.). ISBN 978-0-262-01802-9

hal.science (Global: low place; Hungarian: low place)

(2015) „Deep Learning”. Nature 521 (7553), 436–444. o. DOI:10.1038/nature14539. PMID 26017442.

microsoft.com (Global: 153^rd place; Hungarian: 319^th place)

research.microsoft.com

(2014) „Deep Learning: Methods and Applications”. Foundations and Trends in Signal Processing 7 (3–4), 1–199. o. DOI:10.1561/2000000039. (Hozzáférés: 2014. október 18.)

mit.edu (Global: 415^th place; Hungarian: 753^rd place)

news.mit.edu

Study urges caution when comparing neural networks to the brain (angol nyelven). MIT News | Massachusetts Institute of Technology, 2022. november 2. (Hozzáférés: 2023. december 6.)

nih.gov (Global: 4^th place; Hungarian: 11^th place)

pubmed.ncbi.nlm.nih.gov

(2015) „Deep Learning”. Nature 521 (7553), 436–444. o. DOI:10.1038/nature14539. PMID 26017442.
(2013) „Representation Learning: A Review and New Perspectives”. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8), 1798–1828. o. DOI:10.1109/tpami.2013.50. PMID 23787338. arXiv 1206.5538.
Schmidhuber, J. (2015). „Deep Learning in Neural Networks: An Overview”. Neural Networks 61, 85–117. o. DOI:10.1016/j.neunet.2014.09.003. PMID 25462637. arXiv 1404.7828.
(2017) „Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback”. Nature Communications 8 (1), 138. o. DOI:10.1038/s41467-017-00181-8. PMID 28743932. PMC 5527101.

ncbi.nlm.nih.gov

(2017) „Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback”. Nature Communications 8 (1), 138. o. DOI:10.1038/s41467-017-00181-8. PMID 28743932. PMC 5527101.

nips.cc (Global: low place; Hungarian: low place)

papers.nips.cc

(2007) „Greedy layer-wise training of deep networks”. Advances in neural information processing systems: 153–160.
Lu, Z., Pu, H., Wang, F., Hu, Z., & Wang, L. (2017). The Expressive Power of Neural Networks: A View from the Width Archiválva 2019. február 13-i dátummal a Wayback Machine-ben.. Neural Information Processing Systems, 6231-6239.

researchgate.net (Global: 120^th place; Hungarian: 344^th place)

Rina Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.Online Archiválva 2016. április 19-i dátummal a Wayback Machine-ben.

sanghv.com (Global: low place; Hungarian: low place)

Bengio, Yoshua (2009). „Learning Deep Architectures for AI”. Foundations and Trends in Machine Learning 2 (1), 1–127. o. DOI:10.1561/2200000006. (Hozzáférés: 2015. szeptember 3.)

springer.com (Global: 274^th place; Hungarian: 438^th place)

link.springer.com

Multi-Valued and Universal Binary Neurons. Science & Business Media. DOI: 10.1007/978-1-4757-3115-6 (2000. október 24.). ISBN 978-0-7923-7824-2

techcrunch.com (Global: 187^th place; Hungarian: 597^th place)

Google's AlphaGo AI wins three-match series against the world's best Go player. TechCrunch, 2017. május 25. [2018. június 17-i dátummal az eredetiből archiválva]. (Hozzáférés: 2018. június 17.)

toronto.edu (Global: low place; Hungarian: low place)

cs.toronto.edu

(2012) „ImageNet Classification with Deep Convolutional Neural Networks”. NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada. (Hozzáférés: 2017. május 24.)

utl.pt (Global: low place; Hungarian: low place)

users.isr.ist.utl.pt

Bishop, Christopher M.. Pattern Recognition and Machine Learning. Springer (2006). ISBN 978-0-387-31073-2

web.archive.org (Global: 1^st place; Hungarian: 1^st place)

Google's AlphaGo AI wins three-match series against the world's best Go player. TechCrunch, 2017. május 25. [2018. június 17-i dátummal az eredetiből archiválva]. (Hozzáférés: 2018. június 17.)
Rina Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.Online Archiválva 2016. április 19-i dátummal a Wayback Machine-ben.
Lu, Z., Pu, H., Wang, F., Hu, Z., & Wang, L. (2017). The Expressive Power of Neural Networks: A View from the Width Archiválva 2019. február 13-i dátummal a Wayback Machine-ben.. Neural Information Processing Systems, 6231-6239.

worldcat.org (Global: 5^th place; Hungarian: 22^nd place)

Fradkov, Alexander L. (2020. január 1.). „Early History of Machine Learning”. IFAC-PapersOnLine 53 (2), 1385–1390. o. DOI:10.1016/j.ifacol.2020.12.1888. ISSN 2405-8963.

Deep learning (Hungarian Wikipedia)

archive.org (Global: 6th place; Hungarian: 14th place)

arxiv.org (Global: 69th place; Hungarian: 778th place)

books.google.com (Global: 3rd place; Hungarian: 28th place)

cmu.edu (Global: 1,564th place; Hungarian: 4,669th place)

deeplearning.cs.cmu.edu

doi.org (Global: 2nd place; Hungarian: 8th place)

dx.doi.org

google.hu (Global: 8,839th place; Hungarian: 51st place)

books.google.hu

hal.science (Global: low place; Hungarian: low place)

microsoft.com (Global: 153rd place; Hungarian: 319th place)

research.microsoft.com

mit.edu (Global: 415th place; Hungarian: 753rd place)

news.mit.edu

nih.gov (Global: 4th place; Hungarian: 11th place)

pubmed.ncbi.nlm.nih.gov

ncbi.nlm.nih.gov

nips.cc (Global: low place; Hungarian: low place)

papers.nips.cc

researchgate.net (Global: 120th place; Hungarian: 344th place)

sanghv.com (Global: low place; Hungarian: low place)

springer.com (Global: 274th place; Hungarian: 438th place)

link.springer.com

techcrunch.com (Global: 187th place; Hungarian: 597th place)

toronto.edu (Global: low place; Hungarian: low place)

cs.toronto.edu

utl.pt (Global: low place; Hungarian: low place)

users.isr.ist.utl.pt

web.archive.org (Global: 1st place; Hungarian: 1st place)

worldcat.org (Global: 5th place; Hungarian: 22nd place)

archive.org (Global: 6^th place; Hungarian: 14^th place)

arxiv.org (Global: 69^th place; Hungarian: 778^th place)

books.google.com (Global: 3^rd place; Hungarian: 28^th place)

cmu.edu (Global: 1,564^th place; Hungarian: 4,669^th place)

doi.org (Global: 2^nd place; Hungarian: 8^th place)

google.hu (Global: 8,839^th place; Hungarian: 51^st place)

microsoft.com (Global: 153^rd place; Hungarian: 319^th place)

mit.edu (Global: 415^th place; Hungarian: 753^rd place)

nih.gov (Global: 4^th place; Hungarian: 11^th place)

researchgate.net (Global: 120^th place; Hungarian: 344^th place)

springer.com (Global: 274^th place; Hungarian: 438^th place)

techcrunch.com (Global: 187^th place; Hungarian: 597^th place)

web.archive.org (Global: 1^st place; Hungarian: 1^st place)

worldcat.org (Global: 5^th place; Hungarian: 22^nd place)