-
2
-
-
84904136037
-
Large-scale machine learning with stochastic gradient descent
-
Springer
-
Bottou, L. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010. Springer. 177-186.
-
(2010)
Proceedings of COMPSTAT'2010
, pp. 177-186
-
-
Bottou, L.1
-
4
-
-
77949722130
-
Learning with l1 graph for image analysis
-
Cheng, B.; Yang, J.; Yan, S.; Fu, Y.; and Huang, T. S. 2010. Learning with l1 graph for image analysis. TIP 19(4).
-
(2010)
TIP
, vol.19
, Issue.4
-
-
Cheng, B.1
Yang, J.2
Yan, S.3
Fu, Y.4
Huang, T.S.5
-
5
-
-
1542476948
-
Simple mixture model for sparse overcomplete ica
-
Davies, M., and Mitianoudis, N. 2004. Simple mixture model for sparse overcomplete ica. IEE Proceedings-Vision, Image and Signal Processing 151(1):35-43.
-
(2004)
IEE Proceedings-Vision, Image and Signal Processing
, vol.151
, Issue.1
, pp. 35-43
-
-
Davies, M.1
Mitianoudis, N.2
-
6
-
-
0037418225
-
Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization
-
Donoho, D. L., and Elad, M. 2003. Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proceedings of the National Academy of Sciences 100(5):2197-2202.
-
(2003)
Proceedings of the National Academy of Sciences
, vol.100
, Issue.5
, pp. 2197-2202
-
-
Donoho, D.L.1
Elad, M.2
-
7
-
-
0032182894
-
Data compression and harmonic analysis
-
Donoho, D. L.; Vetterli, M.; DeVore, R. A.; and Daubechies, I. 1998. Data compression and harmonic analysis. Information Theory, IEEE Transactions on 44(6):2435-2476.
-
(1998)
Information Theory, IEEE Transactions on
, vol.44
, Issue.6
, pp. 2435-2476
-
-
Donoho, D.L.1
Vetterli, M.2
De Vore, R.A.3
Daubechies, I.4
-
8
-
-
0029307534
-
De-noising by soft-thresholding. Information Theory
-
Donoho, D. L. 1995. De-noising by soft-thresholding. Information Theory, IEEE Transactions on 41(3):613-627.
-
(1995)
IEEE Transactions on
, vol.41
, Issue.3
, pp. 613-627
-
-
Donoho, D.L.1
-
9
-
-
77956515664
-
Learning fast approximations of sparse coding
-
Gregor, K., and LeCun, Y. 2010. Learning fast approximations of sparse coding. In ICML, 399-406.
-
(2010)
ICML
, pp. 399-406
-
-
Gregor, K.1
LeCun, Y.2
-
12
-
-
0024880831
-
Multilayer feedforward networks are universal approximators
-
Hornik, K.; Stinchcombe, M.; and White, H. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5):359-366.
-
(1989)
Neural Networks
, vol.2
, Issue.5
, pp. 359-366
-
-
Hornik, K.1
Stinchcombe, M.2
White, H.3
-
13
-
-
80052901219
-
Learning a discriminative dictionary for sparse coding via label consistent k-SVD
-
IEEE
-
Jiang, Z.; Lin, Z.; and Davis, L. S. 2011. Learning a discriminative dictionary for sparse coding via label consistent k-svd. In CVPR, 1697-1704. IEEE.
-
(2011)
CVPR
, pp. 1697-1704
-
-
Jiang, Z.1
Lin, Z.2
Davis, L.S.3
-
15
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In NIPS, 1097-1105.
-
(2012)
NIPS
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
17
-
-
84857419890
-
Task-driven dictionary learning
-
Mairal, J.; Bach, F.; and Ponce, J. 2012. Task-driven dictionary learning. TPAMI 34(4):791-804.
-
(2012)
TPAMI
, vol.34
, Issue.4
, pp. 791-804
-
-
Mairal, J.1
Bach, F.2
Ponce, J.3
-
18
-
-
84988474696
-
How to choose an activation function
-
Mhaskar, H. N., and Micchelli, C. A. 1994. How to choose an activation function. In NIPS, 319-326.
-
(1994)
NIPS
, pp. 319-326
-
-
Mhaskar, H.N.1
Micchelli, C.A.2
-
19
-
-
51849128608
-
Sparse coding via thresholding and local competition in neural circuits
-
Rozell, C. J.; Johnson, D. H.; Baraniuk, R. G.; and Olshausen, B. A. 2008. Sparse coding via thresholding and local competition in neural circuits. Neural computation 20(10):2526-2563.
-
(2008)
Neural Computation
, vol.20
, Issue.10
, pp. 2526-2563
-
-
Rozell, C.J.1
Johnson, D.H.2
Baraniuk, R.G.3
Olshausen, B.A.4
-
20
-
-
4544292940
-
The cmu pose, illumination, and expression (pie) database
-
IEEE
-
Sim, T.; Baker, S.; and Bsat, M. 2002. The cmu pose, illumination, and expression (pie) database. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, 46-51. IEEE.
-
(2002)
Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on
, pp. 46-51
-
-
Sim, T.1
Baker, S.2
Bsat, M.3
-
21
-
-
85006120386
-
Supervised sparse analysis and synthesis operators
-
Sprechmann, P.; Litman, R.; Yakar, T. B.; Bronstein, A. M.; and Sapiro, G. 2013. Supervised sparse analysis and synthesis operators. In NIPS, 908-916.
-
(2013)
NIPS
, pp. 908-916
-
-
Sprechmann, P.1
Litman, R.2
Yakar, T.B.3
Bronstein, A.M.4
Sapiro, G.5
-
23
-
-
84897510162
-
On the importance of initialization and momentum in deep learning
-
Sutskever, I.; Martens, J.; Dahl, G.; and Hinton, G. 2013. On the importance of initialization and momentum in deep learning. In ICML, 1139-1147.
-
(2013)
ICML
, pp. 1139-1147
-
-
Sutskever, I.1
Martens, J.2
Dahl, G.3
Hinton, G.4
-
24
-
-
84949773157
-
A joint optimization framework of sparse coding and discriminative clustering
-
Wang, Z.; Yang, Y.; Chang, S.; Li, J.; Fong, S.; and Huang, T. S. 2015a. A joint optimization framework of sparse coding and discriminative clustering. In IJCAI.
-
(2015)
IJCAI
-
-
Wang, Z.1
Yang, Y.2
Chang, S.3
Li, J.4
Fong, S.5
Huang, T.S.6
-
25
-
-
84986313145
-
-
arXiv preprint arXiv:1509.00151
-
Wang, Z.; Chang, S.; Zhou, J.; Wang, M.; and Huang, T. S. 2015b. Learning a task-specific deep architecture for clustering. In arXiv preprint arXiv:1509.00151.
-
(2015)
Learning a Task-specific Deep Architecture for Clustering
-
-
Wang, Z.1
Chang, S.2
Zhou, J.3
Wang, M.4
Huang, T.S.5
-
26
-
-
84907474375
-
Semisupervised hyperspectral classification using task-driven dictionary learning with laplacian regularization
-
Wang, Z.; Nasrabadi, N. M.; and Huang, T. S. 2015. Semisupervised hyperspectral classification using task-driven dictionary learning with laplacian regularization. TGRS 53(3):1161-1173.
-
(2015)
TGRS
, vol.53
, Issue.3
, pp. 1161-1173
-
-
Wang, Z.1
Nasrabadi, N.M.2
Huang, T.S.3
-
28
-
-
34347204252
-
L0-norm minimization for basis selection
-
Wipf, D. P., and Rao, B. D. 2004. l0-norm minimization for basis selection. In NIPS, 1513-1520.
-
(2004)
NIPS
, pp. 1513-1520
-
-
Wipf, D.P.1
Rao, B.D.2
-
29
-
-
61549128441
-
Robust face recognition via sparse representation
-
Wright, J.; Yang, A. Y.; Ganesh, A.; Sastry, S. S.; and Ma, Y. 2009. Robust face recognition via sparse representation. TPAMI 31(2):210-227.
-
(2009)
TPAMI
, vol.31
, Issue.2
, pp. 210-227
-
-
Wright, J.1
Yang, A.Y.2
Ganesh, A.3
Sastry, S.S.4
Ma, Y.5
-
30
-
-
84855424887
-
Image smoothing via l 0 gradient minimization
-
ACM
-
Xu, L.; Lu, C.; Xu, Y.; and Jia, J. 2011. Image smoothing via l 0 gradient minimization. In TOG, volume 30, 174. ACM.
-
(2011)
TOG
, vol.30
, pp. 174
-
-
Xu, L.1
Lu, C.2
Xu, Y.3
Jia, J.4
-
31
-
-
77955995785
-
Supervised translation-invariant sparse coding
-
IEEE
-
Yang, J.; Yu, K.; and Huang, T. 2010. Supervised translation-invariant sparse coding. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 3517-3524. IEEE.
-
(2010)
Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
, pp. 3517-3524
-
-
Yang, J.1
Yu, K.2
Huang, T.3
-
33
-
-
84856686379
-
Adaptive deconvolutional networks for mid and high level feature learning
-
IEEE
-
Zeiler, M. D.; Taylor, G. W.; and Fergus, R. 2011. Adaptive deconvolutional networks for mid and high level feature learning. In ICCV, 2018-2025. IEEE.
-
(2011)
ICCV, 2018-2025
-
-
Zeiler, M.D.1
Taylor, G.W.2
Fergus, R.3
|