-
5
-
-
0005862230
-
Exponentially many local minima for single neurons
-
P. Auer, M. Herbster, and M. Warmuth. Exponentially many local minima for single neurons. In NIPS, 1996.
-
(1996)
NIPS
-
-
Auer, P.1
Herbster, M.2
Warmuth, M.3
-
6
-
-
0037031025
-
Hardness results for neural network approximation problems
-
P. L. Bartlett and S. Ben-David. Hardness results for neural network approximation problems. Theor. Comput. Sci., 284(1):53-66, 2002.
-
(2002)
Theor. Comput. Sci.
, vol.284
, Issue.1
, pp. 53-66
-
-
Bartlett, P.L.1
Ben-David, S.2
-
8
-
-
77955613111
-
Polynomial regression under arbitrary product distributions
-
E. Blais, R. O'Donnell, and K. Wimmer. Polynomial regression under arbitrary product distributions. Machine Learning, 80(2-3):273-294, 2010.
-
(2010)
Machine Learning
, vol.80
, Issue.2-3
, pp. 273-294
-
-
Blais, E.1
O'Donnell, R.2
Wimmer, K.3
-
9
-
-
0026453958
-
Training a 3-node neural network is np-complete
-
A. Blum and R. Rivest. Training a 3-node neural network is np-complete. Neural Networks, 5(1):117-127, 1992.
-
(1992)
Neural Networks
, vol.5
, Issue.1
, pp. 117-127
-
-
Blum, A.1
Rivest, R.2
-
10
-
-
84890527827
-
Improving deep neural networks for lvcsr using rectified linear units and dropout
-
G. Dahl, T. Sainath, and G. Hinton. Improving deep neural networks for lvcsr using rectified linear units and dropout. In ICASSP, 2013.
-
(2013)
ICASSP
-
-
Dahl, G.1
Sainath, T.2
Hinton, G.3
-
11
-
-
33645146449
-
Histograms of oriented gradients for human detection
-
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
-
(2005)
CVPR
-
-
Dalal, N.1
Triggs, B.2
-
12
-
-
84937952344
-
From average case complexity to improper learning complexity
-
A. Daniely, N. Linial, and S. Shalev-Shwartz. From average case complexity to improper learning complexity. In FOCS, 2014.
-
(2014)
FOCS
-
-
Daniely, A.1
Linial, N.2
Shalev-Shwartz, S.3
-
13
-
-
55249114173
-
Agnostically learning halfspaces
-
A. Kalai, A. Klivans, Y. Mansour, and R. Servedio. Agnostically learning halfspaces. SIAM J. Comput., 37(6):1777-1805, 2008.
-
(2008)
SIAM J. Comput.
, vol.37
, Issue.6
, pp. 1777-1805
-
-
Kalai, A.1
Klivans, A.2
Mansour, Y.3
Servedio, R.4
-
15
-
-
35348921036
-
Cryptographic hardness for learning intersections of halfspaces
-
A. Klivans and A. Sherstov. Cryptographic hardness for learning intersections of halfspaces. In FOCS, 2006.
-
(2006)
FOCS
-
-
Klivans, A.1
Sherstov, A.2
-
16
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.3
-
17
-
-
84867135575
-
Building high-level features using large scale unsupervised learning
-
Q. V. Le, M.-A. Ranzato, R. Monga, M. Devin, G. Corrado, K. Chen, J. Dean, and A. Y. Ng. Building high-level features using large scale unsupervised learning. In ICML, 2012.
-
(2012)
ICML
-
-
Le, Q.V.1
Ranzato, M.-A.2
Monga, R.3
Devin, M.4
Corrado, G.5
Chen, K.6
Dean, J.7
Ng, A.Y.8
-
20
-
-
80053458764
-
Large-scale convex minimization with a low-rank constraint
-
S. Shalev-Shwartz, A. Gonen, and O. Shamir. Large-scale convex minimization with a low-rank constraint. In ICML, 2011.
-
(2011)
ICML
-
-
Shalev-Shwartz, S.1
Gonen, A.2
Shamir, O.3
-
21
-
-
84855575451
-
Learning kernel-based halfspaces with the 0-1 loss
-
S. Shalev-Shwartz, O. Shamir, and K. Sridharan. Learning kernel-based halfspaces with the 0-1 loss. SIAM Journal on Computing, 40(6):1623-1646, 2011.
-
(2011)
SIAM Journal on Computing
, vol.40
, Issue.6
, pp. 1623-1646
-
-
Shalev-Shwartz, S.1
Shamir, O.2
Sridharan, K.3
-
22
-
-
70350629124
-
Introduction to the theory of computation
-
M. Sipser. Introduction to the Theory of Computation. Thomson Course Technology, 2006.
-
(2006)
Thomson Course Technology
-
-
Sipser, M.1
-
23
-
-
84897510162
-
On the importance of initialization and momentum in deep learning
-
I. Sutskever, J. Martens, G. Dahl, and G. Hinton. On the importance of initialization and momentum in deep learning. In ICML, 2013.
-
(2013)
ICML
-
-
Sutskever, I.1
Martens, J.2
Dahl, G.3
Hinton, G.4
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