-
1
-
-
84999030430
-
-
2015 Alzheimer's disease facts and figures, Alzheimer's Dement.: J. Alzheimer's Assoc. 11(3) (2015) 332.
-
[1] A. Alzheimers, 2015 Alzheimer's disease facts and figures, Alzheimer's Dement.: J. Alzheimer's Assoc. 11(3) (2015) 332.
-
-
-
Alzheimers, A.1
-
2
-
-
61849113222
-
Rate of progression of mild cognitive impairment to dementia-meta-analysis of 41 robust inception cohort studies
-
[2] Mitchell, A.J., Shiri-Feshki, M., Rate of progression of mild cognitive impairment to dementia-meta-analysis of 41 robust inception cohort studies. Acta Psychiatr. Scand. 119:4 (2009), 252–265.
-
(2009)
Acta Psychiatr. Scand.
, vol.119
, Issue.4
, pp. 252-265
-
-
Mitchell, A.J.1
Shiri-Feshki, M.2
-
3
-
-
84865507195
-
Injury markers predict time to dementia in subjects with MCI and amyloid pathology
-
[3] van Rossum, I.A., Vos, S.J., Burns, L., Knol, D.L., Scheltens, P., Soininen, H., Wahlund, L.-O., Hampel, H., Tsolaki, M., Minthon, L., et al. Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology 79:17 (2012), 1809–1816.
-
(2012)
Neurology
, vol.79
, Issue.17
, pp. 1809-1816
-
-
van Rossum, I.A.1
Vos, S.J.2
Burns, L.3
Knol, D.L.4
Scheltens, P.5
Soininen, H.6
Wahlund, L.-O.7
Hampel, H.8
Tsolaki, M.9
Minthon, L.10
-
4
-
-
41949137974
-
The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods
-
[4] Jack, C.R., et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27:4 (2008), 685–691.
-
(2008)
J. Magn. Reson. Imaging
, vol.27
, Issue.4
, pp. 685-691
-
-
Jack, C.R.1
-
5
-
-
84901587806
-
A Survey on Metric Learning for Feature Vectors and Structured Data
-
arXiv:1306.6709
-
[5] A. Bellet, A. Habrard, M. Sebban, A Survey on Metric Learning for Feature Vectors and Structured Data, arXiv:1306.6709.
-
-
-
Bellet, A.1
Habrard, A.2
Sebban, M.3
-
6
-
-
84998554212
-
-
Distance Metric Learning: A Comprehensive Survey, vol. 2, Michigan State University, 2006, p. 78. East Lansing, MI, USA
-
[6] L. Yang, R. Jin, Distance Metric Learning: A Comprehensive Survey, vol. 2, Michigan State University, 2006, p. 78. East Lansing, MI, USA,.
-
-
-
Yang, L.1
Jin, R.2
-
7
-
-
84879571292
-
Distance metric learning with application to clustering with side-information
-
[7] E.P. Xing, A.Y. Ng, M.I. Jordan, S. Russell, Distance metric learning with application to clustering with side-information, in: Advances in Neural Information Processing Systems, vol. 15, 2003, pp. 505–512.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
, pp. 505-512
-
-
Xing, E.P.1
Ng, A.Y.2
Jordan, M.I.3
Russell, S.4
-
8
-
-
33745887620
-
Neighbourhood components analysis
-
[8] J. Goldberger, G.E. Hinton, S.T. Roweis, R. Salakhutdinov, Neighbourhood components analysis, in: Advances in Neural Information Processing Systems, 2004, pp. 513–520.
-
(2004)
Advances in Neural Information Processing Systems
, pp. 513-520
-
-
Goldberger, J.1
Hinton, G.E.2
Roweis, S.T.3
Salakhutdinov, R.4
-
9
-
-
84898997673
-
Learning a distance metric from relative comparisons
-
Advances in Neural Information Processing Systems, 2004, p. 41.
-
[9] M. Schultz, T. Joachims, Learning a distance metric from relative comparisons, in: Advances in Neural Information Processing Systems, 2004, p. 41.
-
-
-
Schultz, M.1
Joachims, T.2
-
10
-
-
34547996209
-
Information-theoretic metric learning
-
[10] J.V. Davis, B. Kulis, P. Jain, S. Sra, I.S. Dhillon, Information-theoretic metric learning, in: Proceedings of the 24th International Conference on Machine Learning, ACM, New York, NY, USA, 2007, pp. 209–216.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning, ACM, New York, NY, USA
, pp. 209-216
-
-
Davis, J.V.1
Kulis, B.2
Jain, P.3
Sra, S.4
Dhillon, I.S.5
-
11
-
-
33749257955
-
Distance metric learning for large margin nearest neighbor classification
-
[11] K.Q. Weinberger, J. Blitzer, L.K. Saul, Distance metric learning for large margin nearest neighbor classification, in: Advances in Neural Information Processing Systems, 2005, pp. 1473–1480.
-
(2005)
Advances in Neural Information Processing Systems
, pp. 1473-1480
-
-
Weinberger, K.Q.1
Blitzer, J.2
Saul, L.K.3
-
13
-
-
84864062501
-
-
K.-c. Lee, Large margin component analysis, in: Advances in Neural Information Processing Systems, vol. 19, 2007, p. 1385.
-
[13] L. Torresani, K.-c. Lee, Large margin component analysis, in: Advances in Neural Information Processing Systems, vol. 19, 2007, p. 1385.
-
-
-
Torresani, L.1
-
15
-
-
77952550960
-
A new kernelization framework for Mahalanobis distance learning algorithms
-
[15] Chatpatanasiri, R., Korsrilabutr, T., Tangchanachaianan, P., Kijsirikul, B., A new kernelization framework for Mahalanobis distance learning algorithms. Neurocomputing 73:10 (2010), 1570–1579.
-
(2010)
Neurocomputing
, vol.73
, Issue.10
, pp. 1570-1579
-
-
Chatpatanasiri, R.1
Korsrilabutr, T.2
Tangchanachaianan, P.3
Kijsirikul, B.4
-
16
-
-
61749090884
-
Distance metric learning for large margin nearest neighbor classification
-
[16] Weinberger, K.Q., Saul, L.K., Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10 (2009), 207–244.
-
(2009)
J. Mach. Learn. Res.
, vol.10
, pp. 207-244
-
-
Weinberger, K.Q.1
Saul, L.K.2
-
17
-
-
84863020215
-
Learning a mixture of sparse distance metrics for classification and dimensionality reduction
-
[17] Y. Hong, Q. Li, J. Jiang, Z. Tu, Learning a mixture of sparse distance metrics for classification and dimensionality reduction, in: 2011 International Conference on Computer Vision, IEEE, New York, NY, USA, 2011, pp. 906–913.
-
(2011)
2011 International Conference on Computer Vision, IEEE, New York, NY, USA
, pp. 906-913
-
-
Hong, Y.1
Li, Q.2
Jiang, J.3
Tu, Z.4
-
18
-
-
79951945041
-
Local distance functions: a taxonomy, new algorithms, and an evaluation
-
[18] Ramanan, D., Baker, S., Local distance functions: a taxonomy, new algorithms, and an evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 33:4 (2011), 794–806.
-
(2011)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.33
, Issue.4
, pp. 794-806
-
-
Ramanan, D.1
Baker, S.2
-
19
-
-
85162063341
-
Generative local metric learning for nearest neighbor classification
-
[19] Y.-K. Noh, B.-T. Zhang, D.D. Lee, Generative local metric learning for nearest neighbor classification, in: Advances in Neural Information Processing Systems, 2010, pp. 1822–1830.
-
(2010)
Advances in Neural Information Processing Systems
, pp. 1822-1830
-
-
Noh, Y.-K.1
Zhang, B.-T.2
Lee, D.D.3
-
20
-
-
84877749183
-
Parametric local metric learning for nearest neighbor classification
-
[20] J. Wang, A. Kalousis, A. Woznica, Parametric local metric learning for nearest neighbor classification, in: Advances in Neural Information Processing Systems, 2012, pp. 1601–1609.
-
(2012)
Advances in Neural Information Processing Systems
, pp. 1601-1609
-
-
Wang, J.1
Kalousis, A.2
Woznica, A.3
-
21
-
-
39749191312
-
Automatic classification of MR scans in Alzheimer's disease
-
[21] Klöppel, S., et al. Automatic classification of MR scans in Alzheimer's disease. Brain 131:3 (2008), 681–689.
-
(2008)
Brain
, vol.131
, Issue.3
, pp. 681-689
-
-
Klöppel, S.1
-
22
-
-
64949122392
-
Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation
-
[22] Chupin, M., Hammers, A., Liu, R.S., Colliot, O., Burdett, J., Bardinet, E., Duncan, J.S., Garnero, L., Lemieux, L., Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46:3 (2009), 749–761.
-
(2009)
NeuroImage
, vol.46
, Issue.3
, pp. 749-761
-
-
Chupin, M.1
Hammers, A.2
Liu, R.S.3
Colliot, O.4
Burdett, J.5
Bardinet, E.6
Duncan, J.S.7
Garnero, L.8
Lemieux, L.9
-
23
-
-
84923814844
-
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
-
[23] Suk, H.-I., Lee, S.-W., Shen, D., A.D.N. Initiative, et al. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220:2 (2015), 841–859.
-
(2015)
Brain Struct. Funct.
, vol.220
, Issue.2
, pp. 841-859
-
-
Suk, H.-I.1
Lee, S.-W.2
Shen, D.3
-
24
-
-
33846264886
-
Compare: classification of morphological patterns using adaptive regional elements
-
[24] Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C., Compare: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26:1 (2007), 93–105.
-
(2007)
IEEE Trans. Med. Imaging
, vol.26
, Issue.1
, pp. 93-105
-
-
Fan, Y.1
Shen, D.2
Gur, R.C.3
Gur, R.E.4
Davatzikos, C.5
-
25
-
-
84896396327
-
Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis
-
[25] Liu, M., Zhang, D., Shen, D., Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Hum. Brain Mapp. 35:4 (2014), 1305–1319.
-
(2014)
Hum. Brain Mapp.
, vol.35
, Issue.4
, pp. 1305-1319
-
-
Liu, M.1
Zhang, D.2
Shen, D.3
-
26
-
-
84912106417
-
Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition
-
[26] Wu, G., Kim, M., Sanroma, G., Wang, Q., Munsell, B.C., Shen, D., A.D.N. Initiative, et al. Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeuroImage 106 (2015), 34–46.
-
(2015)
NeuroImage
, vol.106
, pp. 34-46
-
-
Wu, G.1
Kim, M.2
Sanroma, G.3
Wang, Q.4
Munsell, B.C.5
Shen, D.6
-
27
-
-
84907019192
-
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
-
[27] Suk, H.-I., Lee, S.-W., Shen, D., A.D.N. Initiative, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101 (2014), 569–582.
-
(2014)
NeuroImage
, vol.101
, pp. 569-582
-
-
Suk, H.-I.1
Lee, S.-W.2
Shen, D.3
-
28
-
-
79955059574
-
Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database
-
[28] Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.-O., Chupin, M., Benali, H., Colliot, O., A.D.N. Initiative, et al. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56:2 (2011), 766–781.
-
(2011)
NeuroImage
, vol.56
, Issue.2
, pp. 766-781
-
-
Cuingnet, R.1
Gerardin, E.2
Tessieras, J.3
Auzias, G.4
Lehéricy, S.5
Habert, M.-O.6
Chupin, M.7
Benali, H.8
Colliot, O.9
-
29
-
-
79952073234
-
Multimodal classification of Alzheimer's disease and mild cognitive impairment
-
[29] Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., A.D.N. Initiative, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 55:3 (2011), 856–867.
-
(2011)
NeuroImage
, vol.55
, Issue.3
, pp. 856-867
-
-
Zhang, D.1
Wang, Y.2
Zhou, L.3
Yuan, H.4
Shen, D.5
-
30
-
-
84925851214
-
Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease
-
[30] Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J., et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans. Biomed. Eng. 62:4 (2015), 1132–1140.
-
(2015)
IEEE Trans. Biomed. Eng.
, vol.62
, Issue.4
, pp. 1132-1140
-
-
Liu, S.1
Liu, S.2
Cai, W.3
Che, H.4
Pujol, S.5
Kikinis, R.6
Feng, D.7
Fulham, M.J.8
-
31
-
-
84927690405
-
View-centralized multi-atlas classification for Alzheimer's disease diagnosis
-
[31] Liu, M., Zhang, D., Shen, D., View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum. Brain Mapp. 36:5 (2015), 1847–1865.
-
(2015)
Hum. Brain Mapp.
, vol.36
, Issue.5
, pp. 1847-1865
-
-
Liu, M.1
Zhang, D.2
Shen, D.3
-
32
-
-
84974687816
-
Relationship induced multi-template learning for diagnosis of Alzheimers disease and mild cognitive impairment
-
[32] Liu, M., Zhang, D., Shen, D., Relationship induced multi-template learning for diagnosis of Alzheimers disease and mild cognitive impairment. IEEE Trans. Med. Imaging 35:6 (2016), 1463–1474.
-
(2016)
IEEE Trans. Med. Imaging
, vol.35
, Issue.6
, pp. 1463-1474
-
-
Liu, M.1
Zhang, D.2
Shen, D.3
-
33
-
-
82755161873
-
Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features
-
(427-e15)
-
[33] Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D., Alzheimer's Disease Neuroimaging Initiative, et al. Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features. Neurobiol. Aging, 33(2), 2012 (427-e15).
-
(2012)
Neurobiol. Aging
, vol.33
, Issue.2
-
-
Li, Y.1
Wang, Y.2
Wu, G.3
Shi, F.4
Zhou, L.5
Lin, W.6
Shen, D.7
-
34
-
-
79956306210
-
Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis
-
[34] McEvoy, L.K., Holland, D., Hagler, D.J. Jr, Fennema-Notestine, C., Brewer, J.B., Dale, A.M., Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. Radiology 259:3 (2011), 834–843.
-
(2011)
Radiology
, vol.259
, Issue.3
, pp. 834-843
-
-
McEvoy, L.K.1
Holland, D.2
Hagler, D.J.3
Fennema-Notestine, C.4
Brewer, J.B.5
Dale, A.M.6
-
35
-
-
85008600022
-
Temporally-constrained group sparse learning for longitudinal data analysis in Alzheimer's disease
-
[35] Jie, B., Liu, M., Liu, J., Zhang, D., Shen, D., Temporally-constrained group sparse learning for longitudinal data analysis in Alzheimer's disease. IEEE Trans. Biomed. Eng., 99, 2016, 1, 10.1109/TBME.2016.2553663.
-
(2016)
IEEE Trans. Biomed. Eng.
, vol.99
, pp. 1
-
-
Jie, B.1
Liu, M.2
Liu, J.3
Zhang, D.4
Shen, D.5
-
36
-
-
84947466198
-
A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI
-
[36] H.-I. Suk, S.-W. Lee, D. Shen, A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Berlin, Heidelberg, Germany, 2015, pp. 573–580.
-
(2015)
International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Berlin, Heidelberg, Germany
, pp. 573-580
-
-
Suk, H.-I.1
Lee, S.-W.2
Shen, D.3
-
37
-
-
84901590013
-
Distance Metric Learning for Kernel Machines
-
arXiv:1208.3422
-
[37] Z. Xu, K.Q. Weinberger, O. Chapelle, Distance Metric Learning for Kernel Machines, arXiv:1208.3422.
-
-
-
Xu, Z.1
Weinberger, K.Q.2
Chapelle, O.3
-
38
-
-
84865088414
-
Learning similarity metric with SVM
-
[38] X. Zhu, P. Gong, Z. Zhao, C. Zhang, Learning similarity metric with SVM, in: The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, New York, NY, USA, 2012, pp. 1–8.
-
(2012)
The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, New York, NY, USA
, pp. 1-8
-
-
Zhu, X.1
Gong, P.2
Zhao, Z.3
Zhang, C.4
-
39
-
-
84998919714
-
-
Spline Models for Observational Data, SIAM, Philadelphia, PA, USA, vol. 59, 1990.
-
[39] G. Wahba, Spline Models for Observational Data, SIAM, Philadelphia, PA, USA, vol. 59, 1990.
-
-
-
Wahba, G.1
-
40
-
-
0001935440
-
Splines minimizing rotation-invariant semi-norms in Sobolev spaces
-
[40] J. Duchon, Splines minimizing rotation-invariant semi-norms in Sobolev spaces, in: Constructive Theory of Functions of Several Variables, Springer, Berlin, Heidelberg, Germany, 1977, pp. 85–100.
-
(1977)
Constructive Theory of Functions of Several Variables, Springer, Berlin, Heidelberg, Germany
, pp. 85-100
-
-
Duchon, J.1
-
41
-
-
0042904903
-
A new point matching algorithm for non-rigid registration
-
[41] Chui, H., Rangarajan, A., A new point matching algorithm for non-rigid registration. Comput. Vis. Image Understand. 89:2 (2003), 114–141.
-
(2003)
Comput. Vis. Image Understand.
, vol.89
, Issue.2
, pp. 114-141
-
-
Chui, H.1
Rangarajan, A.2
-
42
-
-
43049150241
-
Clustering by Means of Medoids
-
North-Holland
-
[42] Kaufman, L., Rousseeuw, P., Clustering by Means of Medoids. 1987, North-Holland.
-
(1987)
-
-
Kaufman, L.1
Rousseeuw, P.2
-
43
-
-
56349158295
-
A simple and fast algorithm for k-medoids clustering
-
[43] Park, H.-S., Jun, C.-H., A simple and fast algorithm for k-medoids clustering. Exp. Syst. Appl. 36:2 (2009), 3336–3341.
-
(2009)
Exp. Syst. Appl.
, vol.36
, Issue.2
, pp. 3336-3341
-
-
Park, H.-S.1
Jun, C.-H.2
-
44
-
-
70350633050
-
Feature weighting using margin and radius based error bound optimization in SVMS
-
[44] H. Do, A. Kalousis, M. Hilario, Feature weighting using margin and radius based error bound optimization in SVMS, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Heidelberg, Germany, 2009, pp. 315–329.
-
(2009)
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Heidelberg, Germany
, pp. 315-329
-
-
Do, H.1
Kalousis, A.2
Hilario, M.3
-
45
-
-
24744440027
-
Voxel-based morphometry of the human brain: methods and applications
-
[45] Mechelli, A., Price, C.J., Friston, K.J., Ashburner, J., Voxel-based morphometry of the human brain: methods and applications. Curr. Med. Imaging Rev. 1:2 (2005), 105–113.
-
(2005)
Curr. Med. Imaging Rev.
, vol.1
, Issue.2
, pp. 105-113
-
-
Mechelli, A.1
Price, C.J.2
Friston, K.J.3
Ashburner, J.4
-
46
-
-
84879726562
-
Advanced normalization tools (ANTS)
-
[46] Avants, B.B., Tustison, N., Song, G., Advanced normalization tools (ANTS). Insight J., 2009, 1–35.
-
(2009)
Insight J.
, pp. 1-35
-
-
Avants, B.B.1
Tustison, N.2
Song, G.3
-
47
-
-
84862997809
-
Freesurfer
-
[47] Fischl, B., Freesurfer. NeuroImage 62:2 (2012), 774–781.
-
(2012)
NeuroImage
, vol.62
, Issue.2
, pp. 774-781
-
-
Fischl, B.1
-
48
-
-
18244406829
-
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
-
[48] Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:3 (2002), 341–355.
-
(2002)
Neuron
, vol.33
, Issue.3
, pp. 341-355
-
-
Fischl, B.1
Salat, D.H.2
Busa, E.3
Albert, M.4
Dieterich, M.5
Haselgrove, C.6
Van Der Kouwe, A.7
Killiany, R.8
Kennedy, D.9
Klaveness, S.10
-
49
-
-
84897502525
-
Natural image bases to represent neuroimaging data
-
[49] A. Gupta, M. Ayhan, A. Maida, Natural image bases to represent neuroimaging data, in: Proceedings of the 30th International Conference on Machine Learning (ICML-13), vol. 28, JMLR Workshop and Conference Proceedings, 2013, pp. 987–994, URL 〈 http://jmlr.org/proceedings/papers/v28/gupta13b.pdf〉.
-
(2013)
Proceedings of the 30th International Conference on Machine Learning (ICML-13), vol. 28, JMLR Workshop and Conference Proceedings
, pp. 987-994
-
-
Gupta, A.1
Ayhan, M.2
Maida, A.3
-
50
-
-
56449089103
-
Extracting and composing robust features with denoising autoencoders
-
[50] P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, ACM, New York, NY, USA, 2008, pp. 1096–1103.
-
(2008)
Proceedings of the 25th International Conference on Machine Learning, ACM, New York, NY, USA
, pp. 1096-1103
-
-
Vincent, P.1
Larochelle, H.2
Bengio, Y.3
Manzagol, P.-A.4
-
51
-
-
69349090197
-
Learning deep architectures for AI
-
[51] Bengio, Y., Learning deep architectures for AI. Found. Trends Mach. Learn. 2:1 (2009), 1–127.
-
(2009)
Found. Trends Mach. Learn.
, vol.2
, Issue.1
, pp. 1-127
-
-
Bengio, Y.1
|