-
1
-
-
84897544737
-
Theano: New features and speed improvements
-
Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua. Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
-
(2012)
Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop
-
-
Bastien, F.1
Lamblin, P.2
Pascanu, R.3
Bergstra, J.4
Goodfellow, I.J.5
Bergeron, A.6
Bouchard, N.7
Bengio, Y.8
-
2
-
-
84857819132
-
Theano: A CPU and GPU math expression compiler
-
June Oral Presentation
-
Bergstra, James, Breuleux, Olivier, Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Desjardins, Guillaume, Turian, Joseph, Warde-Farley, David, and Bengio, Yoshua. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation.
-
(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
Warde-Farley, D.8
Bengio, Y.9
-
3
-
-
85007194411
-
-
ArXiv e-prints, November
-
Choromanska, A., Henaff, M., Mathieu, M., Ben Arous, G., and LeCun, Y. The Loss Surface of Multilayer Networks. ArXiv e-prints, November 2014.
-
(2014)
The Loss Surface of Multilayer Networks
-
-
Choromanska, A.1
Henaff, M.2
Mathieu, M.3
Ben Arous, G.4
LeCun, Y.5
-
4
-
-
84928534967
-
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
-
Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (eds.), Curran Associates, Inc
-
Dauphin, Yann N, Pascanu, Razvan, Gulcehre, Caglar, Cho, Kyunghyun, Ganguli, Surya, and Bengio, Yoshua. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 27, pp. 2933–2941. Curran Associates, Inc., 2014.
-
(2014)
Advances in Neural Information Processing Systems
, vol.27
, pp. 2933-2941
-
-
Dauphin, Y.N.1
Pascanu, R.2
Gulcehre, C.3
Cho, K.4
Ganguli, S.5
Bengio, Y.6
-
7
-
-
84898988737
-
Multi-prediction deep boltzmann machines
-
December
-
Goodfellow, Ian J., Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua. Multi-prediction deep Boltzmann machines. In Neural Information Processing Systems, December 2013a.
-
(2013)
Neural Information Processing Systems
-
-
Goodfellow, I.J.1
Mirza, M.2
Courville, A.3
Bengio, Y.4
-
8
-
-
84893401626
-
-
arXiv preprint
-
Goodfellow, Ian J., Warde-Farley, David, Lamblin, Pascal, Dumoulin, Vincent, Mirza, Mehdi, Pascanu, Razvan, Bergstra, James, Bastien, Frédéric, and Bengio, Yoshua. Pylearn2: a machine learning research library. arXiv preprint arXiv:1308.4214, 2013b.
-
(2013)
Pylearn2: A Machine Learning Research Library
-
-
Goodfellow, I.J.1
Warde-Farley, D.2
Lamblin, P.3
Dumoulin, V.4
Mirza, M.5
Pascanu, R.6
Bergstra, J.7
Bastien, F.8
Bengio, Y.9
-
9
-
-
84893710272
-
Maxout networks
-
Dasgupta, Sanjoy and McAllester, David (eds.)
-
Goodfellow, Ian J., Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua. Maxout networks. In Dasgupta, Sanjoy and McAllester, David (eds.), International Conference on Machine Learning, pp. 1319–1327, 2013c.
-
(2013)
International Conference on Machine Learning
, pp. 1319-1327
-
-
Goodfellow, I.J.1
Warde-Farley, D.2
Mirza, M.3
Courville, A.4
Bengio, Y.5
-
11
-
-
77953183471
-
What is the best multi-stage architecture for object recognition?
-
IEEE
-
Jarrett, Kevin, Kavukcuoglu, Koray, Ranzato, Marc' Aurelio, and LeCun, Yann. What is the best multi-stage architecture for object recognition? In Proc. International Conference on Computer Vision (ICCV’09), pp. 2146–2153. IEEE, 2009.
-
(2009)
Proc. International Conference on Computer Vision (ICCV ’09)
, pp. 2146-2153
-
-
Jarrett, K.1
Kavukcuoglu, K.2
Ranzato, M.A.3
LeCun, Y.4
-
13
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
November
-
LeCun, Yann, Bottou, Leon, Bengio, Yoshua, and Haffner, Patrick. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, November 1998.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
14
-
-
77955998889
-
Convolutional networks and applications in vision
-
IEEE
-
LeCun, Yann, Kavukcuoglu, Koray, and Farabet, Clément. Convolutional networks and applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pp. 253–256. IEEE, 2010.
-
(2010)
Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
, pp. 253-256
-
-
LeCun, Y.1
Kavukcuoglu, K.2
Farabet, C.3
-
15
-
-
34249852033
-
Building a large annotated corpus of english: The penn treebank
-
Marcus, Mitchell P., Santorini, Beatrice, and Marcinkiewicz, Mary Ann. Building a large annotated corpus of english: The penn treebank. COMPUTATIONAL LINGUISTICS, 19(2):313–330, 1993.
-
(1993)
COMPUTATIONAL LINGUISTICS
, vol.19
, Issue.2
, pp. 313-330
-
-
Marcus, M.P.1
Santorini, B.2
Marcinkiewicz, M.A.3
-
16
-
-
0000646059
-
-
chapter 8,. MIT Press, Cambridge
-
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning internal representations by error propagation. volume 1, chapter 8, pp. 318–362. MIT Press, Cambridge, 1986.
-
(1986)
Learning Internal Representations by Error Propagation
, vol.1
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
17
-
-
84937900341
-
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
-
Saxe, Andrew M., McClelland, James L., and Ganguli, Surya. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In ICLR, 2013.
-
(2013)
ICLR
-
-
Saxe, A.M.1
McClelland, J.L.2
Ganguli, S.3
-
18
-
-
84897493971
-
-
Master’s thesis, University of Toronto, Toronto, Canada, January
-
Srivastava, Nitish. Improving Neural Networks with Dropout. Master’s thesis, University of Toronto, Toronto, Canada, January 2013.
-
(2013)
Improving Neural Networks with Dropout
-
-
Srivastava, N.1
-
19
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.
-
(2014)
The Journal of Machine Learning Research
, vol.15
, Issue.1
, pp. 1929-1958
-
-
Srivastava, N.1
Hinton, G.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
20
-
-
84944053926
-
Recurrent neural network regularization
-
Zaremba, Wojciech, Sutskever, Ilya, and Vinyals, Oriol. Recurrent neural network regularization. CoRR, abs/1409.2329, 2014. URL http://arxiv.org/abs/1409.2329.
-
(2014)
CoRR
-
-
Zaremba, W.1
Sutskever, I.2
Vinyals, O.3
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