-
3
-
-
69349090197
-
Learning deep architectures for AI
-
doi: 10.1561/2200000006
-
Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1): 1-127, 2009. doi: 10.1561/2200000006.
-
(2009)
Foundations and Trends in Machine Learning
, vol.2
, Issue.1
, pp. 1-127
-
-
Bengio, Y.1
-
5
-
-
84857819132
-
Theano: A CPU and GPU math expression compiler
-
June. Oral
-
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, and Y. Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral.
-
(2010)
Proceedings of the Python for Scientific Computing Conference (SciPy)
-
-
Bergstra, J.1
Breuleux, O.2
Bastien, F.3
Lamblin, P.4
Pascanu, R.5
Desjardins, G.6
Turian, J.7
Bengio, Y.8
-
7
-
-
84976815495
-
Implementation and tests of low-discrepancy sequences
-
P. Bratley, B. L. Fox, and H. Niederreiter. Implementation and tests of low-discrepancy sequences. Transactions on Modeling and Computer Simulation, (TOMACS), 2(3):195-213, 1992.
-
(1992)
Transactions on Modeling and Computer Simulation, (TOMACS)
, vol.2
, Issue.3
, pp. 195-213
-
-
Bratley, P.1
Fox, B.L.2
Niederreiter, H.3
-
12
-
-
77949522811
-
Why does unsupervised pre-training help deep learning?
-
D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent, and S. 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
-
13
-
-
0002020770
-
On the efficiency of certain quasi-random sequences of points in evaluating multidimensional integrals
-
J. H. Halton. On the efficiency of certain quasi-random sequences of points in evaluating multidimensional integrals. Numerische Mathematik, 2:84-90, 1960.
-
(1960)
Numerische Mathematik
, vol.2
, pp. 84-90
-
-
Halton, J.H.1
-
14
-
-
0042879997
-
Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)
-
DOI 10.1162/106365603321828970
-
N. Hansen, S. D. Müller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11 (1):1-18, 2003. (Pubitemid 37044186)
-
(2003)
Evolutionary Computation
, vol.11
, Issue.1
, pp. 1-18
-
-
Hansen, N.1
Muller, S.D.2
Koumoutsakos, P.3
-
16
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
DOI 10.1162/neco.2006.18.7.1527
-
G. E. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554, 2006. (Pubitemid 44024729)
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.-W.3
-
18
-
-
84856930049
-
Sequential model-based optimization for general algorithm configuration
-
Extended version as UBC Tech report TR-
-
F. Hutter, H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In LION-5, 2011. Extended version as UBC Tech report TR-2010-10.
-
(2011)
LION-5
, pp. 2010-10
-
-
Hutter, F.1
Hoos, H.2
Leyton-Brown, K.3
-
19
-
-
26444479778
-
Optimization by simulated annealing
-
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220 (4598):671-680, 1983.
-
(1983)
Science
, vol.220
, Issue.4598
, pp. 671-680
-
-
Kirkpatrick, S.1
Gelatt, C.D.2
Vecchi, M.P.3
-
20
-
-
0033361754
-
Simulation-based optimization with stochastic approximation using common random numbers
-
November 1999. doi: doi:10.1287/mnsc.45.11.1570
-
N. L. Kleinman, J. C. Spall, and D. Q. Naiman. Simulation-based optimization with stochastic approximation using common random numbers. Management Science, 45(11):1570-1578, November 1999. doi: doi:10.1287/mnsc.45. 11.1570.
-
Management Science
, vol.45
, Issue.11
, pp. 1570-1578
-
-
Kleinman, N.L.1
Spall, J.C.2
Naiman, D.Q.3
-
21
-
-
34547967782
-
An empirical evaluation of deep architectures on problems with many factors of variation
-
Z. Ghahramani, editor, ACM
-
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In Z. Ghahramani, editor, Proceedings of the Twenty-fourth International Conference on Machine Learning (ICML'07), pages 473-480. ACM, 2007.
-
(2007)
Proceedings of the Twenty-fourth International Conference on Machine Learning (ICML'07)
, pp. 473-480
-
-
Larochelle, H.1
Erhan, D.2
Courville, A.3
Bergstra, J.4
Bengio, Y.5
-
22
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
November
-
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998a.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
Lecun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
23
-
-
0001857994
-
Efficient backprop
-
G. Orr and K. Muller, editors, Springer
-
Y. LeCun, L. Bottou, G. Orr, and K. Muller. Efficient backprop. In G. Orr and K. Muller, editors, Neural Networks: Tricks of the Trade. Springer, 1998b.
-
(1998)
Neural Networks: Tricks of the Trade.
-
-
Lecun, Y.1
Bottou, L.2
Orr, G.3
Muller, K.4
-
25
-
-
0018468345
-
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
-
M. D. McKay, R. J. Beckman, and W. J. Conover. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2): 239-245, May 1979. doi: doi:10.2307/1268522. (Pubitemid 9452235)
-
(1979)
Technometrics
, vol.21
, Issue.2
, pp. 239-245
-
-
McKay, M.D.1
Beckman, R.J.2
Conover, W.J.3
-
26
-
-
4344713562
-
Choosing search heuristics by non-stationary reinforcement learning
-
A. Nareyek. Choosing search heuristics by non-stationary reinforcement learning. Applied Optimization, 86:523-544, 2003.
-
(2003)
Applied Optimization
, vol.86
, pp. 523-544
-
-
Nareyek, A.1
-
27
-
-
0001854616
-
Assessing relevance determination methods using DELVE
-
C. M. Bishop, editor,. Springer-Verlag
-
R. M. Neal. Assessing relevance determination methods using DELVE. In C. M. Bishop, editor, Neural Networks and Machine Learning, pages 97-129. Springer-Verlag, 1998.
-
(1998)
Neural Networks and Machine Learning
, pp. 97-129
-
-
Neal, R.M.1
-
28
-
-
0000238336
-
A simplex method for function minimization
-
J. A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7: 308-313, 1965.
-
(1965)
The Computer Journal
, vol.7
, pp. 308-313
-
-
Nelder, J.A.1
Mead, R.2
-
29
-
-
0001770043
-
A direct search optimization method that models the objective and constraint functions by linear interpolation
-
M. J. D. Powell. A direct search optimization method that models the objective and constraint functions by linear interpolation. Advances in Optimization and Numerical Analysis, pages 51-67, 1994.
-
(1994)
Advances in Optimization and Numerical Analysis
, pp. 51-67
-
-
Powell, M.J.D.1
-
32
-
-
79952760123
-
Parameter screening and optimisation for ILP using designed experiments
-
February
-
A. Srinivasan and G. Ramakrishnan. Parameter screening and optimisation for ILP using designed experiments. Journal of Machine Learning Research, 12:627-662, February 2011.
-
(2011)
Journal of Machine Learning Research
, vol.12
, pp. 627-662
-
-
Srinivasan, A.1
Ramakrishnan, G.2
-
33
-
-
56449089103
-
Extracting and composing robust features with denoising autoencoders
-
W. W. Cohen, A. McCallum, and S. T. Roweis, editors, ACM
-
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol. Extracting and composing robust features with denoising autoencoders. In W. W. Cohen, A. McCallum, and S. T. Roweis, editors, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08), pages 1096-1103. ACM, 2008.
-
(2008)
Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)
, pp. 1096-1103
-
-
Vincent, P.1
Larochelle, H.2
Bengio, Y.3
Manzagol, P.4
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