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We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input.
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Exploring compositional high order pattern potentials for structured output learning
Li, Yujia, Tarlow, Daniel, and Zemel, Richard · 2013
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Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
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The shape boltzmann machine: a strong model of object shape
Eslami, SM Ali, Heess, Nicolas, Williams, Christopher KI, and Winn, John · 2014
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Ronneberger, Olaf, Fischer, Philipp, and Brox, Thomas · 2015
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Deep learning for semantic part segmentation with high-level guidance
Tsogkas, Stavros, Kokkinos, Iasonas, Papandreou, George, and Vedaldi, Andrea · 2015
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Conditional random fields as recurrent neural networks
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Sequence to sequence learning with neural networks
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Pinheiro, P., Lin, T.-Y., Collobert, R., , and Dollar, P · 2016
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Training deep neural networks via direct loss minimization
Song, Yang, Schwing, Alexander, Zemel, Richard, and Urtasun, Raquel · 2016
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Deep reinforcement learning with double q-learning
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End-to-end learning for structured prediction energy networks
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