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Many machine learning problems require the prediction of multi-dimensional labels.
A method for finding projections onto the intersection of convex sets in hilbert spaces
Boyle, James P and Dykstra, Richard L · 1986
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Efficient inference with cardinality-based clique potentials
Gupta, Rahul, Diwan, Ajit A, and Sarawagi, Sunita · 2007
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Efficient projections onto the l 1-ball for learning in high dimensions
Duchi, John, Shalev-Shwartz, Shai, Singer, Yoram, and Chandra, Tushar · 2008
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Lifted probabilistic inference with counting formulas
Milch, Brian, Zettlemoyer, Luke S, Kersting, Kristian, Haimes, Michael, and Kaelbling, Leslie Pack · 2008
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Posterior regularization for structured latent variable models
Ganchev, Kuzman, Gillenwater, Jennifer, Taskar, Ben, et al · 2010
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L1 projections with box constraints
Gupta, Mithun Das, Kumar, Sanjeev, and Xiao, Jing · 2010
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Hop-map: Efficient message passing with high order potentials
Tarlow, Daniel, Givoni, Inmar E, and Zemel, Richard S · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, John, Hazan, Elad, and Singer, Yoram · 2011
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Alternating Projection Methods
Escalante, Ren and Raydan, Marcos · 2011
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Cardinality restricted boltzmann machines
Swersky, Kevin, Sutskever, Ilya, Tarlow, Daniel, Zemel, Richard S, Salakhutdinov, Ruslan R, and Adams, Ryan P · 2012
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Fast exact inference for recursive cardinality models
Tarlow, Daniel, Swersky, Kevin, Zemel, Richard S, Adams, Ryan P, and Frey, Brendan J · 2012
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, Martín, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S., Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Goodfellow, Ian, Harp, Andrew, Irving, Geoffrey, Isard, Michael, Jia, Yangqing, Jozefowicz, Rafal, Kaiser, Lukasz, Kudlur, Manjunath, Levenberg, Josh, Mané, Dan, Monga, Rajat, Moore, Sherry, Murray, Derek, Olah, Chris, Schuster, Mike, Shlens, Jonathon, Steiner, Benoit, Sutskever, Ilya, Talwar, Kunal, Tucker, Paul, Vanhoucke, Vincent, Vasudevan, Vijay, Viégas, Fernanda, Vinyals, Oriol, Warden, Pete, Wattenberg, Martin, Wicke, Martin, Yu, Yuan, and Zheng, Xiaoqiang · 2015
Cited alongside, same era.
Learning deep structured models
Chen, Liang-Chieh, Schwing, Alexander, Yuille, Alan, and Urtasun, Raquel · 2015
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Visual recognition by counting instances: A multi-instance cardinality potential kernel
Learning to learn by gradient descent by gradient descent
Andrychowicz, Marcin, Denil, Misha, Gomez, Sergio, Hoffman, Matthew W, Pfau, David, Schaul, Tom, and de Freitas, Nando · 2016
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Structured prediction energy networks
Belanger, David and McCallum, Andrew · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Ma, Xuezhe and Hovy, Eduard · 2016
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Unrolled generative adversarial networks
Metz, Luke, Poole, Ben, Pfau, David, and Sohl-Dickstein, Jascha · 2016
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Input-convex deep networks
Amos, Brandon and Kolter, J Zico · 2017
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End-to-end learning for structured prediction energy networks
Belanger, David, Yang, Bishan, and McCallum, Andrew · 2017
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Hajimirsadeghi, Hossein, Yan, Wang, Vahdat, Arash, and Mori, Greg · 2015
Cited alongside, same era.
Gradient-based hyperparameter optimization through reversible learning
Maclaurin, Dougal, Duvenaud, David, and Adams, Ryan · 2015
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Fully connected deep structured networks
Schwing, Alexander G and Urtasun, Raquel · 2015
Cited alongside, same era.
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 · 2015
Cited alongside, same era.
Highway and residual networks learn unrolled iterative estimation
Greff, Klaus, Srivastava, Rupesh K, and Schmidhuber, Jürgen · 2017
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Deep value networks learn to evaluate and iteratively refine structured outputs
Gygli, Michael, Norouzi, Mohammad, and Angelova, Anelia · 2017
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