18. 参考文献

ACB17

Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, 214–223. PMLR, 2017.

BCD+94

L. Bottou, C. Cortes, J.S. Denker, H. Drucker, I. Guyon, L.D. Jackel, Y. LeCun, U.A. Muller, E. Sackinger, P. Simard, and V. Vapnik. Comparison of classifier methods: a case study in handwritten digit recognition. In Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5), volume 2, 77–82 vol.2. 1994. doi:10.1109/ICPR.1994.576879.

CBD+90

Y Le Cun, B Boser, J S Denker, R E Howard, W Habbard, L D Jackel, and D Henderson. Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems 2, pages 396–404. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, June 1990.

Cyb89

G Cybenko. Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems, 2(4):303–314, December 1989. doi:10.1007/bf02551274.

DDF+90

Scott Deerwester, Susan T Dumais, George W Furnas, Thomas K Landauer, and Richard Harshman. Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci., 41(6):391–407, September 1990. doi:10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9.

DDS+09

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. June 2009. doi:10.1109/CVPR.2009.5206848.

Fuk80

K Fukushima. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36(4):193–202, 1980. doi:10.1007/BF00344251.

GPAM+14

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Z Ghahramani, M Welling, C Cortes, N Lawrence, and K Q Weinberger, editors, Advances in Neural Information Processing Systems, volume 27. Curran Associates, Inc., 2014.

HMvdW+20

Charles R. Harris, K. Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. Array programming with NumPy. Nature, 585:357–362, 2020. doi:10.1038/s41586-020-2649-2.

HZRS15

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, December 2015. doi:10.1109/iccv.2015.123.

HS06

G E Hinton and R R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507, July 2006. doi:10.1126/science.1127647.

Hor91

Kurt Hornik. Approximation capabilities of multilayer feedforward networks. Neural Netw., 4(2):251–257, January 1991. doi:10.1016/0893-6080(91)90009-T.

HSW89

Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural Netw., 2(5):359–366, January 1989. doi:10.1016/0893-6080(89)90020-8.

KSH12

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 2012.

LBD+89

Y LeCun, B Boser, J S Denker, D Henderson, R E Howard, W Hubbard, and L D Jackel. Backpropagation applied to handwritten zip code recognition. Neural Comput., 1(4):541–551, December 1989. doi:10.1162/neco.1989.1.4.541.

LBBH98

Y Lecun, L Bottou, Y Bengio, and P Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278–2324, November 1998. doi:10.1109/5.726791.

LS99

D D Lee and H S Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, October 1999. doi:10.1038/44565.

LS00

Daniel Lee and H Sebastian Seung. Algorithms for non-negative matrix factorization. In T Leen, T Dietterich, and V Tresp, editors, Advances in Neural Information Processing Systems, volume 13. MIT Press, 2000.

LLPS93

Moshe Leshno, Vladimir Ya Lin, Allan Pinkus, and Shimon Schocken. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw., 6(6):861–867, January 1993. doi:10.1016/S0893-6080(05)80131-5.

Lin07

Chih-Jen Lin. Projected gradient methods for nonnegative matrix factorization. Neural Comput., 19(10):2756–2779, October 2007. doi:10.1162/neco.2007.19.10.2756.

MP43

Warren S McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys., 5(4):115–133, December 1943. doi:10.1007/BF02478259.

MSC+13

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst., 2013.

MYZ13

Tomas Mikolov, Wen-Tau Yih, and Geoffrey Zweig. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 746–751. Atlanta, Georgia, June 2013. Association for Computational Linguistics.

MCCD13

Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In Yoshua Bengio and Yann LeCun, editors, 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings. 2013.

MP17

Marvin Minsky and Seymour A Papert. Perceptrons: An introduction to computational geometry. The MIT Press, 2017. ISBN 9780262343930. doi:10.7551/mitpress/11301.001.0001.

PGM+19

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.

Pin99

Allan Pinkus. Approximation theory of the MLP model in neural networks. Acta Numer., 8:143–195, January 1999. doi:10.1017/S0962492900002919.

RMC15

missing journal in Radford2015-ci

Ros58

F Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev., 65(6):386–408, November 1958. doi:10.1037/h0042519.

SZ14

missing journal in Simonyan2014-wx

SLJ+15

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. IEEE, June 2015. doi:10.1109/cvpr.2015.7298594.

vdMH08

Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 9(86):2579–2605, 2008.

16

岡谷 貴之. 画像認識のための深層学習の研究動向 : 畳込みニューラルネットワークとその利用法の発展(ニューラルネットワーク研究のフロンティア). 人工知能, 31(2):169–179, 2016. doi:10.11517/jjsai.31.2\_169.