רשת קונבולוציה (Hebrew Wikipedia)

Analysis of information sources in references of the Wikipedia article "רשת קונבולוציה" in Hebrew language version.

refsWebsite

Global rank Hebrew rank

9doi.org

2^nd place

10^th place

3nih.gov

4^th place

18^th place

2ieee.org

652^nd place

820^th place

1hebrew-academy.org.il

2,518^th place

17^th place

1sciencedirect.com

149^th place

87^th place

1drive.google.com

1,343^rd place

354^th place

1ebscohost.com

1,786^th place

1,956^th place

doi.org

dx.doi.org

M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev, Application of the residue number system to reduce hardware costs of the convolutional neural network implementation, Mathematics and Computers in Simulation 177, 2020-11-01, עמ' 232–243 doi: 10.1016/j.matcom.2020.04.031
Alastair Hudson, Ten things you need to know about company law, Second edition. | Abingdon, Oxon; New York, NY: Routledge, 2017.: Routledge, 2017-07-06, עמ' 9–12
K. Fukushima, Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics 36, 1980, עמ' 193–202 doi: 10.1007/BF00344251
Dan Ciregan, Ueli Meier, Jürgen Schmidhuber, Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012-06, עמ' 3642–3649 doi: 10.1109/CVPR.2012.6248110
Yann LeCun, Yoshua Bengio, Geoffrey Hinton, Deep learning, Nature 521, 2015-05-28, עמ' 436–444 doi: 10.1038/nature14539
T. Kitamura, S. Takei, Speaker recognition model using two-dimensional mel-cepstrum and predictive neural network, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, IEEE doi: 10.1109/icslp.1996.607972
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM 60, 2017-05-24, עמ' 84–90 doi: 10.1145/3065386
Håkan Wieslander, Philip J. Harrison, Gabriel Skogberg, Sonya Jackson, Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images, IEEE Journal of Biomedical and Health Informatics 25, 2021-02, עמ' 371–380 doi: 10.1109/JBHI.2020.2996300
Y. W. Chow, R. Pietranico, A. Mukerji, Studies of oxygen binding energy to hemoglobin molecule, Biochemical and Biophysical Research Communications 66, 1975-10-27, עמ' 1424–1431 doi: 10.1016/0006-291x(75)90518-5

drive.google.com

Zhang-1990-AppliedOptics-SIANN.pdf, Google Docs

ebscohost.com

search.ebscohost.com

Hamed Habibi Aghdam, Elnaz Jahani Heravi, Guide to convolutional neural networks: a practical application to traffic-sign detection and classification, 2017, ISBN 978-3-319-57550-6. (בenglish)

hebrew-academy.org.il

terms.hebrew-academy.org.il

רֶשֶׁת עֲצַבִּית מִתְקַפֶּלֶת במילון טכנולוגיית המידע: רשתות עצביות (תשע"א), באתר האקדמיה ללשון העברית

ieee.org

ieeexplore.ieee.org

Dan Ciregan, Ueli Meier, Jürgen Schmidhuber, Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012-06, עמ' 3642–3649 doi: 10.1109/CVPR.2012.6248110
Håkan Wieslander, Philip J. Harrison, Gabriel Skogberg, Sonya Jackson, Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images, IEEE Journal of Biomedical and Health Informatics 25, 2021-02, עמ' 371–380 doi: 10.1109/JBHI.2020.2996300

nih.gov

pubmed.ncbi.nlm.nih.gov

K. Fukushima, Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics 36, 1980, עמ' 193–202 doi: 10.1007/BF00344251
Yann LeCun, Yoshua Bengio, Geoffrey Hinton, Deep learning, Nature 521, 2015-05-28, עמ' 436–444 doi: 10.1038/nature14539
Y. W. Chow, R. Pietranico, A. Mukerji, Studies of oxygen binding energy to hemoglobin molecule, Biochemical and Biophysical Research Communications 66, 1975-10-27, עמ' 1424–1431 doi: 10.1016/0006-291x(75)90518-5

sciencedirect.com

M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev, Application of the residue number system to reduce hardware costs of the convolutional neural network implementation, Mathematics and Computers in Simulation 177, 2020-11-01, עמ' 232–243 doi: 10.1016/j.matcom.2020.04.031