-
1
-
-
0034296402
-
Generalized discriminant analysis using a kernel approach
-
Gaston Baudat and Fatiha Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10):2385-2404, 2000.
-
(2000)
Neural Computation
, vol.12
, Issue.10
, pp. 2385-2404
-
-
Baudat, G.1
Anouar, F.2
-
3
-
-
84864073449
-
Greedy layer-wise training of deep networks
-
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. Greedy layer-wise training of deep networks. In Advances in Neural Information Processing Systems 19, pages 153-160, 2006.
-
(2006)
Advances in Neural Information Processing Systems
, vol.19
, pp. 153-160
-
-
Bengio, Y.1
Lamblin, P.2
Popovici, D.3
Larochelle, H.4
-
5
-
-
33847215211
-
Stochastic gradient learning in neural networks
-
Leon Bottou. Stochastic gradient learning in neural networks. In Proceedings of Neuro-Nîmes, 1991.
-
(1991)
Proceedings of Neuro-nîmes
-
-
Bottou, L.1
-
6
-
-
33846313242
-
Introduction to Statistical Learning Theory
-
Olivier Bousquet, Stéphane Boucheron, and Gabor Lugosi. Introduction to statistical learning theory. In Olivier Bousquet, Ulrike von Luxburg, and Gunnar Rätsch, editors, Advanced Lectures on Machine Learning, volume 3176, pages 169-207. Springer, 2004. (Pubitemid 39741632)
-
(2004)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
, vol.3176
, pp. 169-207
-
-
Bousquet, O.1
Boucheron, S.2
Lugosi, G.3
-
7
-
-
33750713303
-
Accurate error bounds for the eigenvalues of the kernel matrix
-
Mikio L. Braun. Accurate bounds for the eigenvalues of the kernel matrix. Journal of Machine Learning Research, 7:2303-2328, 2006. (Pubitemid 44708008)
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2303-2328
-
-
Braun, M.L.1
-
9
-
-
0031189914
-
Multitask Learning
-
Rich Caruana. Multitask learning. Machine Learning, 28(1):41-75, 1997. (Pubitemid 127507169)
-
(1997)
Machine Learning
, vol.28
, Issue.1
, pp. 41-75
-
-
Caruana, R.1
-
12
-
-
34249753618
-
Support-vector networks
-
Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
-
(1995)
Machine Learning
, vol.20
, Issue.3
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
13
-
-
77949522811
-
Why does unsupervised pre-training help deep learning?
-
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11:625-660, 2010.
-
(2010)
Journal of Machine Learning Research
, vol.11
, pp. 625-660
-
-
Erhan, D.1
Bengio, Y.2
Courville, A.3
Manzagol, P.4
Vincent, P.5
Bengio, S.6
-
14
-
-
84860644702
-
Measuring invariances in deep networks
-
Ian Goodfellow, Quoc Le, Andrew Saxe, and Andrew Y. Ng. Measuring invariances in deep networks. In Advances in Neural Information Processing Systems 22, pages 646-654, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.22
, pp. 646-654
-
-
Goodfellow, I.1
Le, Q.2
Saxe, A.3
Ng, A.Y.4
-
15
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
DOI 10.1162/neco.2006.18.7.1527
-
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006. (Pubitemid 44024729)
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.-W.3
-
16
-
-
33645410496
-
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
-
January
-
David H. Hubel and Torsten N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of Physiology, 160:106-154, January 1962.
-
(1962)
The Journal of Physiology
, vol.160
, pp. 106-154
-
-
Hubel, D.H.1
Wiesel, T.N.2
-
18
-
-
0025254722
-
A time-delay neural network architecture for isolated word recognition
-
Kevin J. Lang, Alex H. Waibel, and Geoffrey E. Hinton. A time-delay neural network architecture for isolated word recognition. Neural Networks, 3(1):23-43, 1990.
-
(1990)
Neural Networks
, vol.3
, Issue.1
, pp. 23-43
-
-
Lang, K.J.1
Waibel, A.H.2
Hinton, G.E.3
-
19
-
-
59449087310
-
Exploring strategies for training deep neural networks
-
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin. Exploring strategies for training deep neural networks. Journal of Machine Learning Research, 10:1-40, 2009.
-
(2009)
Journal of Machine Learning Research
, vol.10
, pp. 1-40
-
-
Larochelle, H.1
Bengio, Y.2
Louradour, J.3
Lamblin, P.4
-
20
-
-
0342898730
-
Generalization and network design strategies
-
Elsevier, An extended version was published as a technical report of the University of Toronto
-
Yann LeCun. Generalization and network design strategies. In Connectionism in Perspective. Elsevier, 1989. An extended version was published as a technical report of the University of Toronto.
-
(1989)
Connectionism in Perspective
-
-
LeCun, Y.1
-
21
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(1):2278-2324, 1998.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.1
, pp. 2278-2324
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
23
-
-
0038633559
-
Learning discriminative and invariant nonlinear features
-
Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, and Klaus-Robert Müller. Learning discriminative and invariant nonlinear features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, 2003.
-
(2003)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.25
, Issue.5
, pp. 623-628
-
-
Mika, S.1
Rätsch, G.2
Weston, J.3
Schölkopf, B.4
Smola, A.J.5
Müller, K.-R.6
-
25
-
-
0035272287
-
An introduction to kernel-based learning algorithms
-
Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, and Bernhard Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-202, 2001.
-
(2001)
IEEE Transactions on Neural Networks
, vol.12
, Issue.2
, pp. 181-202
-
-
Müller, K.1
Mika, S.2
Rätsch, G.3
Tsuda, K.4
Schölkopf, B.5
-
28
-
-
0036314487
-
Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex
-
Dario L. Ringach. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. The Journal of Neurophysiology, 88(1):455-463, 2002. (Pubitemid 34755364)
-
(2002)
Journal of Neurophysiology
, vol.88
, Issue.1
, pp. 455-463
-
-
Ringach, D.L.1
-
29
-
-
0022471098
-
Learning representations by back-propagating errors
-
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533-536, 1986. (Pubitemid 16025374)
-
(1986)
Nature
, vol.323
, Issue.6088
, pp. 533-536
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
32
-
-
0347243182
-
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
-
Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299-1319, 1998. (Pubitemid 128463674)
-
(1998)
Neural Computation
, vol.10
, Issue.5
, pp. 1299-1319
-
-
Scholkopf, B.1
Smola, A.2
Muller, K.-R.3
-
33
-
-
0032594954
-
Input space versus feature space in kernel-based methods
-
Bernhard Schölkopf, Sebastian Mika, Chris J. C. Burges, Philipp Knirsch, Klaus-Robert Müller, Gunnar Rätsch, and Alex J. Smola. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, 1999.
-
(1999)
IEEE Transactions on Neural Networks
, vol.10
, Issue.5
, pp. 1000-1017
-
-
Schölkopf, B.1
Mika, S.2
Burges, C.J.C.3
Knirsch, P.4
Müller, K.5
Rätsch, G.6
Smola, A.J.7
-
34
-
-
24644511277
-
Object recognition with features inspired by visual cortex
-
Thomas Serre, Lior Wolf, and Tomaso Poggio. Object recognition with features inspired by visual cortex. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 994-1000, 2005.
-
(2005)
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, vol.2
, pp. 994-1000
-
-
Serre, T.1
Wolf, L.2
Poggio, T.3
-
35
-
-
75849157565
-
Mathematics of the neural response
-
Steve Smale, Lorenzo Rosasco, Jack Bouvrie, Andrea Caponnetto, and Tomaso Poggio. Mathematics of the neural response. Foundations of Computational Mathematics, 10(1):67-91, 2010.
-
(2010)
Foundations of Computational Mathematics
, vol.10
, Issue.1
, pp. 67-91
-
-
Smale, S.1
Rosasco, L.2
Bouvrie, J.3
Caponnetto, A.4
Poggio, T.5
-
36
-
-
0032098361
-
The connection between regularization operators and support vector kernels
-
DOI 10.1016/S0893-6080(98)00032-X, PII S089360809800032X
-
Alex J. Smola, Bernhard Schölkopf, and Klaus-Robert Müller. The connection between regularization operators and support vector kernels. Neural Networks, 11(4):637-649, 1998. (Pubitemid 28400264)
-
(1998)
Neural Networks
, vol.11
, Issue.4
, pp. 637-649
-
-
Smola, A.J.1
Scholkopf, B.2
Muller, K.-R.3
-
38
-
-
80555158407
-
-
Technical report, Massachusetts Institute of Technology
-
Andre Wibisono, Jake Bouvrie, Lorenzo Rosasco, and Tomaso Poggio. Learning and invariance in a family of hierarchical kernels. Technical report, Massachusetts Institute of Technology, 2010.
-
(2010)
Learning and Invariance in a Family of Hierarchical Kernels
-
-
Wibisono, A.1
Bouvrie, J.2
Rosasco, L.3
Poggio, T.4
|