-
1
-
-
84958264664
-
-
Abadi, Martin, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.
-
(2016)
Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems
-
-
Abadi, M.1
Agarwal, A.2
Barham, P.3
Brevdo, E.4
Chen, Z.5
Citro, C.6
Corrado, G.S.7
Davis, A.8
Dean, J.9
Devin, M.10
-
4
-
-
0001263162
-
Globally trained handwritten word recognizer using spatial representation, convolutional neural networks, and hidden markov models
-
Bengio, Yoshua, LeCun, Yann, and Henderson, Donnie. Globally trained handwritten word recognizer using spatial representation, convolutional neural networks, and hidden markov models. Advances in neural information processing systems, pp. 937-937, 1994.
-
(1994)
Advances in Neural Information Processing Systems
, pp. 937
-
-
Bengio, Y.1
LeCun, Y.2
Henderson, D.3
-
5
-
-
0000377218
-
Projected Newton methods for optimization problems with simple constraints
-
Bertsekas, Dimitri P. Projected newton methods for optimization problems with simple constraints. SIAM Journal on control and Optimization, 20(2):221-246, 1982.
-
(1982)
SIAM Journal on Control and Optimization
, vol.20
, Issue.2
, pp. 221-246
-
-
Bertsekas, D.P.1
-
6
-
-
0034345420
-
Nonmonotone spectral projected gradient methods on convex sets
-
Birgin, Ernesto G, Martínez, José Mario, and Raydan, Marcos. Nonmonotone spectral projected gradient methods on convex sets. SIAM Journal on Optimization, 10(4):1196-1211, 2000.
-
(2000)
SIAM Journal on Optimization
, vol.10
, Issue.4
, pp. 1196-1211
-
-
Birgin, E.G.1
Martínez, J.M.2
Raydan, M.3
-
8
-
-
80051762104
-
Distributed optimization and statistical learning via the alternating direction method of multipliers
-
Boyd, Stephen, Parikh, Ncal, Chu, Eric, Pcleato, Borja, and Eckstein, Jonathan. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1):1-122, 2011.
-
(2011)
Foundations and Trends® in Machine Learning
, vol.3
, Issue.1
, pp. 1-122
-
-
Boyd, S.1
Parikh, N.2
Chu, E.3
Pcleato, B.4
Eckstein, J.5
-
9
-
-
85015444377
-
-
Brockman, Greg, Cheung, Vicki, Pettersson, Ludwig, Schneider, Jonas, Schulman, John, Tang, Jie, and Zaremba, Wojciech. Openai gym. arXiv preprint arXiv: 1606.01540, 2016.
-
(2016)
Openai Gym
-
-
Brockman, G.1
Cheung, V.2
Pettersson, L.3
Schneider, J.4
Schulman, J.5
Tang, J.6
Zaremba, W.7
-
10
-
-
84969930631
-
Learning deep structured models
-
Chen, Liang-Chieh, Schwing, Alexander G, Yuille, Alan L, and Urtasun, Raquel. Learning deep structured models. In Proceedings of the International Conference on Machine Learning, 2015.
-
(2015)
Proceedings of the International Conference on Machine Learning
-
-
Chen, L.-C.1
Schwing, A.G.2
Yuille, A.L.3
Urtasun, R.4
-
12
-
-
80052250414
-
Adaptive subgradient methods for online learning and stochastic optimization
-
Duchi, John, Hazan, Elad, and Singer, Yoram. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121-2159, 2011.
-
(2011)
The Journal of Machine Learning Research
, vol.12
, pp. 2121-2159
-
-
Duchi, J.1
Hazan, E.2
Singer, Y.3
-
13
-
-
84937849144
-
Generative adversarial nets
-
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua. Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.
-
(2014)
Advances in Neural Information Processing Systems
, pp. 2672-2680
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
14
-
-
84998579328
-
Continuous deep q-learning with modelbased acceleration
-
Gu, Shixiang, Lillicrap, Timothy, Sutskever, Ilya, and Levine, Sergey. Continuous deep q-learning with modelbased acceleration. In Proceedings of the International Conference on Machine Learning, 2016.
-
(2016)
Proceedings of the International Conference on Machine Learning
-
-
Gu, S.1
Lillicrap, T.2
Sutskever, I.3
Levine, S.4
-
15
-
-
84958589374
-
-
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.
-
(2015)
Deep Residual Learning for Image Recognition
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
18
-
-
77956208484
-
Multilabel text classification for automated tag suggestion
-
Katakis, Ioannis, Tsoumakas, Grigorios, and Vlahavas, Ioannis. Multilabel text classification for automated tag suggestion. ECML PKDD discovery challenge, 75, 2008.
-
(2008)
ECML PKDD Discovery Challenge
, pp. 75
-
-
Katakis, I.1
Tsoumakas, G.2
Vlahavas, I.3
-
21
-
-
84876231242
-
Imagcnet classification with deep convolutional neural networks
-
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E. Imagcnet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097-1105, 2012.
-
(2012)
Advances in Neural Information Processing Systems
, pp. 1097-1105
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
22
-
-
35148893484
-
A tutorial on energy-based learning
-
LeCun, Yann, Chopra, Sumit, Hadsell, Raia, Ranzato, M, and Huang, F. A tutorial on energy-based learning. Predicting structured data, 1:0, 2006.
-
(2006)
Predicting Structured Data
, vol.1
, pp. 0
-
-
LeCun, Y.1
Chopra, S.2
Hadsell, R.3
Ranzato, M.4
Huang, F.5
-
23
-
-
84965135289
-
-
Lillicrap, Timothy P, Hunt, Jonathan J, Pritzel, Alexander, Heess, Nicolas, Erez, Tom, Tassa, Yuval, Silver, David, and Wierstra, Daan. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
-
(2015)
Continuous Control with Deep Reinforcement Learning
-
-
Lillicrap, T.P.1
Hunt, J.J.2
Pritzel, A.3
Heess, N.4
Erez, T.5
Tassa, Y.6
Silver, D.7
Wierstra, D.8
-
24
-
-
60549110283
-
Convex piecewise-linear fitting
-
Magnani, Alessandro and Boyd, Stephen P. Convex piecewise-linear fitting. Optimization and Engineering, 10(1):1-17, 2009.
-
(2009)
Optimization and Engineering
, vol.10
, Issue.1
, pp. 1-17
-
-
Magnani, A.1
Boyd, S.P.2
-
25
-
-
84924051598
-
Human-level control through deep reinforcement learning
-
Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A, Veness, Joel, Bellemare, Marc G, Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K, Ostrovski, Georg, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.
-
(2015)
Nature
, vol.518
, Issue.7540
, pp. 529-533
-
-
Mnih, V.1
Kavukcuoglu, K.2
Silver, D.3
Rusu, A.A.4
Veness, J.5
Bellemare, M.G.6
Graves, A.7
Riedmiller, M.8
Fidjeland, A.K.9
Ostrovski, G.10
-
27
-
-
85048688297
-
-
Trelgol Publishing USA
-
Oliphant, Travis E. A guide to NumPy, volume 1. Trelgol Publishing USA, 2006.
-
(2006)
A Guide to NumPy
, vol.1
-
-
Oliphant, T.E.1
-
28
-
-
80555140075
-
Scikit-learn: Machine learning in python
-
Pedregosa, Fabian, Varoquaux, Gael, Gramfort, Alexandre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondcl, Mathieu, Prettcnhofer, Peter, Weiss, Ron, Dubourg, Vincent, et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825-2830, 2011.
-
(2011)
The Journal of Machine Learning Research
, vol.12
, pp. 2825-2830
-
-
Pedregosa, F.1
Varoquaux, G.2
Gramfort, A.3
Michel, V.4
Thirion, B.5
Grisel, O.6
Blondcl, M.7
Prettcnhofer, P.8
Weiss, R.9
Dubourg, V.10
-
29
-
-
84863373241
-
Conditional neural fields
-
Peng, Jian, Bo, Liefeng, and Xu, Jinbo. Conditional neural fields. In Advances in neural information processing systems, pp. 1419-1427, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, pp. 1419-1427
-
-
Peng, J.1
Bo, L.2
Xu, J.3
-
31
-
-
80053162579
-
Sum-product networks: A new deep architecture
-
Spain, July 14-17, 2011
-
Poon, Hoifung and Domingos, Pedro. Sum-product networks: A new deep architecture. In UAI 2011 Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, July 14-17, 2011, pp. 337-346, 2011.
-
(2011)
UAI 2011 Proceedings of the Twenty-seventh Conference on Uncertainty in Artificial Intelligence, Barcelona
, pp. 337-346
-
-
Poon, H.1
Domingos, P.2
-
32
-
-
84862297087
-
(Approximate) subgradient methods for structured prediction
-
Ratliff, Nathan D, Bagnell, J Andrew, and Zinkevich, Martin. (Approximate) subgradient methods for structured prediction. In International Conference on Artificial Intelligence and Statistics, pp. 380-387, 2007.
-
(2007)
International Conference on Artificial Intelligence and Statistics
, pp. 380-387
-
-
Ratliff, N.D.1
Bagnell, J.A.2
Zinkevich, M.3
-
33
-
-
84921817164
-
Learning representations by back-propagating errors
-
Rumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J. Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 1988.
-
(1988)
Cognitive Modeling
, vol.5
, Issue.3
, pp. 1
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
34
-
-
0028734063
-
Parameterisation of a stochastic model for human face identification
-
IEEE
-
Samaria, Ferdinando S and Harter, Andy C. Parameterisation of a stochastic model for human face identification. In Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on, pp. 138-142. IEEE, 1994.
-
(1994)
Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on
, pp. 138-142
-
-
Samaria, F.S.1
Harter, A.C.2
-
37
-
-
85162015906
-
Bundle methods for machine learning
-
Platt, J. C, Koller, D., Singer, Y., and Roweis, S. T. eds., Curran Associates, Inc.
-
Smola, Alex J., Vishwanathan, S.v. n., and Le, Quoc V. Bundle methods for machine learning. In Platt, J. C, Koller, D., Singer, Y., and Roweis, S. T. (eds.), Advances in Neural Information Processing Systems 20, pp. 1377-1384. Curran Associates, Inc., 2008.
-
(2008)
Advances in Neural Information Processing Systems
, vol.20
, pp. 1377-1384
-
-
Smola, A.J.1
Vishwanathan, S.N.2
Le, Q.V.3
-
38
-
-
84937522268
-
Going deeper with convolutions
-
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, pp. 1-9
-
-
Szegedy, C.1
Liu, W.2
Jia, Y.3
Sermanet, P.4
Reed, S.5
Anguelov, D.6
Erhan, D.7
Vanhoucke, V.8
Rabinovich, A.9
-
39
-
-
31844442382
-
Learning structured prediction models: A large margin approach
-
ACM
-
Taskar, Ben, Chatalbashev, Vassil, Koller, Daphne, and Guestrin, Carlos. Learning structured prediction models: A large margin approach. In Proceedings of the 22nd International Conference on Machine Learning, pp. 896-903. ACM, 2005.
-
(2005)
Proceedings of the 22nd International Conference on Machine Learning
, pp. 896-903
-
-
Taskar, B.1
Chatalbashev, V.2
Koller, D.3
Guestrin, C.4
-
40
-
-
84872292044
-
Mujoco: A physics engine for model-based control
-
IEEE
-
Todorov, Emanuel, Erez, Tom, and Tassa, Yuval. Mujoco: A physics engine for model-based control. In 2012 1EEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026-5033. IEEE, 2012.
-
(2012)
2012 1EEE/RSJ International Conference on Intelligent Robots and Systems
, pp. 5026-5033
-
-
Todorov, E.1
Erez, T.2
Tassa, Y.3
-
41
-
-
24944537843
-
Large margin methods for structured and interdependent output variables
-
Tsochantaridis, Ioannis, Joachims, Thorsten, Hofmann, Thomas, and Altun, Yasemin. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, 2005.
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 1453-1484
-
-
Tsochantaridis, I.1
Joachims, T.2
Hofmann, T.3
Altun, Y.4
-
42
-
-
80052236046
-
Muían: A Java library for multi-label learning
-
Jul.
-
Tsoumakas, Grigorios, Spyromitros-Xioufis, Eleftherios, Vilcek, Jozef, and Vlahavas, Ioannis. Muían: A java library for multi-label learning. Journal of Machine Learning Research, 12(Jul):2411-2414, 2011.
-
(2011)
Journal of Machine Learning Research
, vol.12
, pp. 2411-2414
-
-
Tsoumakas, G.1
Spyromitros-Xioufis, E.2
Vilcek, J.3
Vlahavas, I.4
-
45
-
-
84973861983
-
Conditional random fields as recurrent neural networks
-
Zheng, Shuai, Jayasumana, Sadeep, Romera-Paredes, Bernardino, Vineet, Vibhav, Su, Zhizhong, Du, Dalong, Huang, Chang, and Torr, Philip HS. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1529-1537, 2015.
-
(2015)
Proceedings of the IEEE International Conference on Computer Vision
, pp. 1529-1537
-
-
Zheng, S.1
Jayasumana, S.2
Romera-Paredes, B.3
Vineet, V.4
Su, Z.5
Du, D.6
Huang, C.7
Torr, P.H.S.8
|