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We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution.
Adaptive mixtures of local experts
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Robust real-time face detection
P. Viola and M. J. Jones · 2004
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Label embedding trees for large multi-class tasks
S. Bengio, J. Weston, and D. Grangier · 2010
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Cascade object detection with deformable part models
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Fast and balanced: Efficient label tree learning for large scale object recognition
J. Deng, S. Satheesh, A. C. Berg, and F. Li · 2011
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Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition
J. Deng, J. Krause, A. C. Berg, and L. Fei-Fei · 2012
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Learning where to attend with deep architectures for image tracking
M. Denil, L. Bazzani, H. Larochelle, and N. de Freitas · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Learning factored representations in a deep mixture of experts
D. Eigen, M. Ranzato, and I. Sutskever · 2013
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Probabilistic label trees for efficient large scale image classification
B. Liu, F. Sadeghi, M. Tappen, O. Shamir, and C. Liu · 2013
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Deep convolutional network cascade for facial point detection
Y. Sun, X. Wang, and X. Tang · 2013
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Multiple object recognition with visual attention
J. Ba, V. Mnih, and K. Kavukcuoglu · 2014
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Dadiannao: A machine-learning supercomputer
Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, et al · 2014
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Conditional computation in neural networks for faster models
E. Bengio, P.-L. Bacon, J. Pineau, and D. Precup · 2015
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Draw: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra · 2015
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Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan · 2015
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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A convolutional neural network cascade for face detection
H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua · 2015
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Dynamic capacity networks
A. Almahairi, N. Ballas, T. Cooijmans, Y. Zheng, H. Larochelle, and A. C. Courville · 2016
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L. Denoyer and P. Gallinari · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
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Labeled faces in the wild: Updates and new reporting procedures
G. B. H. E. Learned-Miller · 2014
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Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, et al · 2014
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Deep networks with internal selective attention through feedback connections
M. F. Stollenga, J. Masci, F. Gomez, and J. Schmidhuber · 2014
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http://torch.ch/
Torch
Cited in the paper.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto
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Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean · 2017
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