Backpropagation (Romanian Wikipedia)
Analysis of information sources in references of the Wikipedia article "Backpropagation" in Romanian language version.
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adsabs.harvard.edu (Global: 14th place; Romanian: 29th place)
- Schmidhuber, Jürgen (). „Deep Learning”. Scholarpedia. 10 (11): 32832. Bibcode:2015SchpJ..1032832S. doi:10.4249/scholarpedia.32832.
- Rumelhart; Hinton; Williams (). „Learning representations by back-propagating errors” (PDF). Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (). „Learning representations by back-propagating errors”. Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
- Tan, Hong Hui; Lim, King Han (). „Review of second-order optimization techniques in artificial neural networks backpropagation”. IOP Conference Series: Materials Science and Engineering. 495 (1): 012003. Bibcode:2019MS&E..495a2003T. doi:10.1088/1757-899X/495/1/012003.
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (). „Deep learning”. Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
- Dreyfus, Stuart E. (). „Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”. Journal of Guidance, Control, and Dynamics. 13 (5): 926–928. Bibcode:1990JGCD...13..926D. doi:10.2514/3.25422.
archive.org (Global: 6th place; Romanian: 19th place)
- Kelley, Henry J. (). „Gradient theory of optimal flight paths”. ARS Journal. 30 (10): 947–954. doi:10.2514/8.5282.
- Robbins, H.; Monro, S. (). „A Stochastic Approximation Method”. The Annals of Mathematical Statistics. 22 (3): 400. doi:10.1214/aoms/1177729586.
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (). „Learning representations by back-propagating errors”. Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (). „Deep learning”. Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
- Buckland, Matt; Collins, Mark (). AI Techniques for Game Programming. Boston: Premier Press. ISBN 1-931841-08-X.
- Hecht-Nielsen, Robert (). Neurocomputing. Internet Archive. Reading, Mass. : Addison-Wesley Pub. Co. pp. 124–125. ISBN 978-0-201-09355-1.
- Dreyfus, Stuart E. (). „Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”. Journal of Guidance, Control, and Dynamics. 13 (5): 926–928. Bibcode:1990JGCD...13..926D. doi:10.2514/3.25422.
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (). „8. Learning Internal Representations by Error Propagation”. În Rumelhart, David E.; McClelland, James L. Parallel Distributed Processing : Explorations in the Microstructure of Cognition. 1 : Foundations. Cambridge: MIT Press. ISBN 0-262-18120-7.
- Hertz, John (). Introduction to the theory of neural computation. Krogh, Anders., Palmer, Richard G. Redwood City, Calif.: Addison-Wesley. pp. 8. ISBN 0-201-50395-6. OCLC 21522159.
- Sejnowski, Terrence J. (). The deep learning revolution. Cambridge, Massachusetts London, England: The MIT Press. ISBN 978-0-262-03803-4.
- Chang, Franklin; Dell, Gary S.; Bock, Kathryn (). „Becoming syntactic”. Psychological Review. 113 (2): 234–272. doi:10.1037/0033-295x.113.2.234. PMID 16637761.
arxiv.org (Global: 49th place; Romanian: 119th place)
- Un robot va completa această citare în curând. Clic aici pentru a trece mai în față arXiv:.
- Ramachandran. „Searching for Activation Functions”. arXiv:1710.05941
. - Misra. „Mish: A Self Regularized Non-Monotonic Activation Function”. arXiv:1908.08681 .
auburn.edu (Global: 8,344th place; Romanian: 7,926th place)
eng.auburn.edu
- Wiliamowski, Bogdan; Yu, Hao (iunie 2010). „Improved Computation for Levenberg–Marquardt Training” (PDF). IEEE Transactions on Neural Networks and Learning Systems. 21 (6). Arhivat din original (PDF) la . Accesat în .
books.google.com (Global: 3rd place; Romanian: 8th place)
- Leibniz, Gottfried Wilhelm Freiherr von (). The Early Mathematical Manuscripts of Leibniz: Translated from the Latin Texts Published by Carl Immanuel Gerhardt with Critical and Historical Notes (Leibniz published the chain rule in a 1676 memoir) (în engleză). Open court publishing Company. p. 90. ISBN 9780598818461.
- Griewank, Andreas; Walther, Andrea (). Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition. SIAM. ISBN 978-0-89871-776-1.
- Alpaydin, Ethem (). Introduction to Machine Learning. MIT Press. ISBN 978-0-262-01243-0.
deeplearningbook.org (Global: low place; Romanian: low place)
- Goodfellow, Bengio & Courville 2016, p. 214. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (). „6.5 Back-Propagation and Other Differentiation Algorithms”. Deep Learning. MIT Press. pp. 200–220. ISBN 9780262035613. Parametru necunoscut
|section-url=ignorat (ajutor) - Goodfellow, Bengio & Courville 2016, p. 200. , "The term back-propagation is often misunderstood as meaning the whole learning algorithm for multilayer neural networks. Backpropagation refers only to the method for computing the gradient, while other algorithms, such as stochastic gradient descent, is used to perform learning using this gradient." Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (). „6.5 Back-Propagation and Other Differentiation Algorithms”. Deep Learning. MIT Press. pp. 200–220. ISBN 9780262035613. Parametru necunoscut
|section-url=ignorat (ajutor)
doi.org (Global: 2nd place; Romanian: 3rd place)
- Linnainmaa, Seppo (). „Taylor expansion of the accumulated rounding error”. BIT Numerical Mathematics. 16 (2): 146–160. doi:10.1007/bf01931367.
- Schmidhuber, Jürgen (). „Deep learning in neural networks: An overview”. Neural Networks. 61: 85–117. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
- Schmidhuber, Jürgen (). „Deep Learning”. Scholarpedia. 10 (11): 32832. Bibcode:2015SchpJ..1032832S. doi:10.4249/scholarpedia.32832.
- Kelley, Henry J. (). „Gradient theory of optimal flight paths”. ARS Journal. 30 (10): 947–954. doi:10.2514/8.5282.
- Robbins, H.; Monro, S. (). „A Stochastic Approximation Method”. The Annals of Mathematical Statistics. 22 (3): 400. doi:10.1214/aoms/1177729586.
- Rumelhart; Hinton; Williams (). „Learning representations by back-propagating errors” (PDF). Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
- Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (). „Learning representations by back-propagating errors”. Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
- Tan, Hong Hui; Lim, King Han (). „Review of second-order optimization techniques in artificial neural networks backpropagation”. IOP Conference Series: Materials Science and Engineering. 495 (1): 012003. Bibcode:2019MS&E..495a2003T. doi:10.1088/1757-899X/495/1/012003.
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (). „Deep learning”. Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
- Rodríguez, Omar Hernández; López Fernández, Jorge M. (). „A Semiotic Reflection on the Didactics of the Chain Rule”. The Mathematics Enthusiast. 7 (2): 321–332. doi:10.54870/1551-3440.1191. Accesat în .
- Dreyfus, Stuart (). „The numerical solution of variational problems”. Journal of Mathematical Analysis and Applications. 5 (1): 30–45. doi:10.1016/0022-247x(62)90004-5.
- Dreyfus, Stuart E. (). „Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”. Journal of Guidance, Control, and Dynamics. 13 (5): 926–928. Bibcode:1990JGCD...13..926D. doi:10.2514/3.25422.
- Dreyfus, Stuart (). „The computational solution of optimal control problems with time lag”. IEEE Transactions on Automatic Control. 18 (4): 383–385. doi:10.1109/tac.1973.1100330.
- Anderson, James A.; Rosenfeld, ed. (). Talking Nets: An Oral History of Neural Networks (în engleză). The MIT Press. doi:10.7551/mitpress/6626.003.0016. ISBN 978-0-262-26715-1.
- Chang, Franklin; Dell, Gary S.; Bock, Kathryn (). „Becoming syntactic”. Psychological Review. 113 (2): 234–272. doi:10.1037/0033-295x.113.2.234. PMID 16637761.
- Janciauskas, Marius; Chang, Franklin (). „Input and Age-Dependent Variation in Second Language Learning: A Connectionist Account”. Cognitive Science. 42 (Suppl Suppl 2): 519–554. doi:10.1111/cogs.12519. PMC 6001481 . PMID 28744901.
- Fitz, Hartmut; Chang, Franklin (). „Language ERPs reflect learning through prediction error propagation”. Cognitive Psychology (în engleză). 111: 15–52. doi:10.1016/j.cogpsych.2019.03.002. PMID 30921626.
ieee.org (Global: 912th place; Romanian: 1,058th place)
spectrum.ieee.org
- „Photonic Chips Curb AI Training's Energy Appetite - IEEE Spectrum”. spectrum.ieee.org (în engleză). Accesat în .
incompleteideas.net (Global: low place; Romanian: low place)
- Sutton, Richard S.; Barto, Andrew G. (). „11.1 TD-Gammon”. Reinforcement Learning: An Introduction (ed. 2nd). Cambridge, MA: MIT Press.
janbasktraining.com (Global: low place; Romanian: low place)
- „Decoding the Power of Backpropagation: A Deep Dive into Advanced Neural Network Techniques”. janbasktraining.com (în engleză).
mit.edu (Global: 476th place; Romanian: 582nd place)
direct.mit.edu
- Anderson, James A.; Rosenfeld, ed. (). Talking Nets: An Oral History of Neural Networks (în engleză). The MIT Press. doi:10.7551/mitpress/6626.003.0016. ISBN 978-0-262-26715-1.
neuralnetworksanddeeplearning.com (Global: low place; Romanian: low place)
- Aceasta rezultă din Nielsen (2015), și înseamnă că înmulțirea (la stânga) cu matricea corespunde convertirii valorilor de ieșire ale stratului în valori de intrare ale stratului : coloanele corespund coordonatelor de intrare, rândurile corespund coordonatelor de ieșire. Nielsen, Michael A. (). „How the backpropagation algorithm works”. Neural Networks and Deep Learning. Determination Press.
- Secțiunea se bazează pe și rezumă Nielsen (2015). Nielsen, Michael A. (). „How the backpropagation algorithm works”. Neural Networks and Deep Learning. Determination Press.
neurips.cc (Global: low place; Romanian: low place)
proceedings.neurips.cc
- Pomerleau, Dean A. (). „ALVINN: An Autonomous Land Vehicle in a Neural Network”. Advances in Neural Information Processing Systems. Morgan-Kaufmann. 1.
nih.gov (Global: 5th place; Romanian: 6th place)
ncbi.nlm.nih.gov
- Schmidhuber, Jürgen (). „Deep learning in neural networks: An overview”. Neural Networks. 61: 85–117. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (). „Deep learning”. Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
- Chang, Franklin; Dell, Gary S.; Bock, Kathryn (). „Becoming syntactic”. Psychological Review. 113 (2): 234–272. doi:10.1037/0033-295x.113.2.234. PMID 16637761.
- Janciauskas, Marius; Chang, Franklin (). „Input and Age-Dependent Variation in Second Language Learning: A Connectionist Account”. Cognitive Science. 42 (Suppl Suppl 2): 519–554. doi:10.1111/cogs.12519. PMC 6001481 . PMID 28744901.
- Fitz, Hartmut; Chang, Franklin (). „Language ERPs reflect learning through prediction error propagation”. Cognitive Psychology (în engleză). 111: 15–52. doi:10.1016/j.cogpsych.2019.03.002. PMID 30921626.
sudoc.fr (Global: 4,362nd place; Romanian: 5,028th place)
- Le Cun, Yann (). Modèles connexionnistes de l'apprentissage (Teză). Paris, France: Université Pierre et Marie Curie. Arhivat din original la . Accesat în .
tools.wmflabs.org (Global: 1,945th place; Romanian: 140th place)
- Un robot va completa această citare în curând. Clic aici pentru a trece mai în față arXiv:.
toronto.edu (Global: low place; Romanian: low place)
cs.toronto.edu
- Rumelhart; Hinton; Williams (). „Learning representations by back-propagating errors” (PDF). Nature. 323 (6088): 533–536. Bibcode:1986Natur.323..533R. doi:10.1038/323533a0.
umt.edu (Global: low place; Romanian: low place)
scholarworks.umt.edu
- Rodríguez, Omar Hernández; López Fernández, Jorge M. (). „A Semiotic Reflection on the Didactics of the Chain Rule”. The Mathematics Enthusiast. 7 (2): 321–332. doi:10.54870/1551-3440.1191. Accesat în .
web.archive.org (Global: 1st place; Romanian: 1st place)
- Wiliamowski, Bogdan; Yu, Hao (iunie 2010). „Improved Computation for Levenberg–Marquardt Training” (PDF). IEEE Transactions on Neural Networks and Learning Systems. 21 (6). Arhivat din original (PDF) la . Accesat în .
- Olazaran Rodriguez, Jose Miguel. A historical sociology of neural network research. PhD Dissertation. University of Edinburgh, 1991.
- Le Cun, Yann (). Modèles connexionnistes de l'apprentissage (Teză). Paris, France: Université Pierre et Marie Curie. Arhivat din original la . Accesat în .
werbos.com (Global: low place; Romanian: low place)
- Werbos, Paul (). „Applications of advances in nonlinear sensitivity analysis” (PDF). System modeling and optimization. Springer. pp. 762–770. Accesat în .
wikidata.org (Global: 10th place; Romanian: 2nd place)
- Derivata funcției de cost este un Covector(d), deoarece funcția de cost este o funcție cu valori scalare de mai multe variabile.
worldcat.org (Global: 4th place; Romanian: 9th place)
- Bryson, Arthur E. (). „A gradient method for optimizing multi-stage allocation processes”. Proceedings of the Harvard Univ. Symposium on digital computers and their applications, 3–6 April 1961. Cambridge: Harvard University Press. OCLC 498866871.
- Hertz, John (). Introduction to the theory of neural computation. Krogh, Anders., Palmer, Richard G. Redwood City, Calif.: Addison-Wesley. pp. 8. ISBN 0-201-50395-6. OCLC 21522159.
wpengine.com (Global: low place; Romanian: low place)
coeieor.wpengine.com
- Mizutani, Eiji; Dreyfus, Stuart; Nishio, Kenichi (iulie 2000). „On derivation of MLP backpropagation from the Kelley-Bryson optimal-control gradient formula and its application” (PDF). Proceedings of the IEEE International Joint Conference on Neural Networks.