Backpropagation (Romanian Wikipedia)
Analysis of information sources in references of the Wikipedia article "Backpropagation" in Romanian language version.
refsWebsite
Global rank
Romanian rank
18th place
27th place
3rd place
6th place
5th place
11th place
low place
low place
6th place
23rd place
43rd place
2nd place
3,290th place
99th place
low place
low place
7,446th place
5,129th place
low place
low place
low place
low place
1st place
1st place
low place
low place
low place
low place
low place
low place
archive.org
- Hecht-Nielsen, Robert (). Neurocomputing. Internet Archive. Reading, Mass. : Addison-Wesley Pub. Co. pp. 124–125. ISBN 978-0-201-09355-1.
- 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.
arxiv.org
- Un robot va completa această citare în curând. Clic aici pentru a trece mai în față arXiv:[1].
- Ramachandran. „Searching for Activation Functions”. arXiv:1710.05941
. - Misra. „Mish: A Self Regularized Non-Monotonic Activation Function”. arXiv:1908.08681 .
auburn.edu
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).
books.google.com
- 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
- Goodfellow, Bengio & Courville 2016, p. 214.
- 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."
doi.org
- 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.
harvard.edu
adsabs.harvard.edu
- 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.
ieee.org
spectrum.ieee.org
- „Photonic Chips Curb AI Training's Energy Appetite - IEEE Spectrum”. spectrum.ieee.org (în engleză). Accesat în .
incompleteideas.net
- Sutton, Richard S.; Barto, Andrew G. (). „11.1 TD-Gammon”. Reinforcement Learning: An Introduction (ed. 2nd). Cambridge, MA: MIT Press.
janbasktraining.com
- „Decoding the Power of Backpropagation: A Deep Dive into Advanced Neural Network Techniques”. janbasktraining.com (în engleză).
mit.edu
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.
neurips.cc
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
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
- Le Cun, Yann (). Modèles connexionnistes de l'apprentissage (Teză). Paris, France: Université Pierre et Marie Curie.
tools.wmflabs.org
- Un robot va completa această citare în curând. Clic aici pentru a trece mai în față arXiv:[1].
toronto.edu
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
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
- Olazaran Rodriguez, Jose Miguel. A historical sociology of neural network research. PhD Dissertation. University of Edinburgh, 1991.
werbos.com
- Werbos, Paul (). „Applications of advances in nonlinear sensitivity analysis” (PDF). System modeling and optimization. Springer. pp. 762–770. Accesat în .
wikidata.org
- Derivata funcției de cost este un Covector(d), deoarece funcția de cost este o funcție cu valori scalare de mai multe variabile.
worldcat.org
- 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. p. 8. ISBN 0-201-50395-6. OCLC 21522159.
wpengine.com
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.