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Attention has long been proposed by psychologists as important for effectively dealing with the enormous sensory stimulus available in the neocortex.
Shifter circuits: A computational strategy for dynamic aspects of visual processing
C. H. Anderson and D. C. Van Essen · 1987
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Hybrid Monte Carlo
S. Duane, A. D. Kennedy, B. J Pendleton, and D. Roweth · 1987
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A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
B. A. Olshausen, C. H. Anderson, and D. C. Van Essen · 1993
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Modeling visual-attention via selective tuning
J. K. Tsotsos, S. M. Culhane, W. Y. K. Wai, Y. H. Lai, N. Davis, and F. Nuflo · 1995
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Fast normalized cross-correlation, 1995
J. P. Lewis · 1995
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Modeling the manifolds of images of handwritten digits
Geoffrey E. Hinton, Peter Dayan, and Michael Revow · 1997
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Attention and primary visual cortex
M. I. Posner and C. D. Gilbert · 1999
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Visual attention: Insights from brain imaging
N Kanwisher and E Wojciulik · 2000
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Lucas-kanade 20 years on: A unifying framework
Simon Baker and Iain Matthews · 2002
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How does our visual system achieve shift and size invariance?, 2004
Laurenz Wiskott · 2004
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A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y. W. Teh · 2006
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Reducing the dimensionality of data with neural networks
G. E. Hinton and R. Salakhutdinov · 2006
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Deep Boltzmann machines
R. Salakhutdinov and G. Hinton · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Cited alongside, same era.
Using fast weights to improve persistent contrastive divergence
T. Tieleman and G. E. Hinton · 2009
Cited alongside, same era.
A backward progression of attentional effects in the ventral stream
E. A. Buffalo, P. Fries, R. Landman, H. Liang, and R. Desimone · 2010
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From learning models of natural image patches to whole image restoration
Daniel Zoran and Yair Weiss · 2011
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On Deep Generative Models with Applications to Recognition
Marc’Aurelio Ranzato, Joshua Susskind, Volodymyr Mnih, and Geoffrey Hinton · 2011
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A hierarchical generative model of recurrent object-based attention in the visual cortex
D. P. Reichert, P. Seriès, and A. J. Storkey · 2011
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Acoustic modeling using deep belief networks
A. Mohamed, G. Dahl, and G. Hinton · 2011
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Computer Vision - Algorithms and Applications
Richard Szeliski · 2011
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Deep mixtures of factor analysers
Yichuan Tang, Ruslan Salakhutdinov, and Geoffrey E. Hinton · 2012
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What and where: a Bayesian inference theory of attention
S. Chikkerur, T. Serre, C. Tan, and T. Poggio · 2010
Cited alongside, same era.
Learning to combine foveal glimpses with a third-order boltzmann machine
Hugo Larochelle and Geoffrey E. Hinton · 2010
Cited alongside, same era.
Convolutional learning of spatio-temporal features
Graham W. Taylor, Rob Fergus, Yann LeCun, and Christoph Bregler · 2010
Cited alongside, same era.
MCMC using Hamiltonian dynamics
R. M. Neal · 2010
Cited alongside, same era.
Multi-pie
Ralph Gross, Iain Matthews, Jeffrey F. Cohn, Takeo Kanade, and Simon Baker · 2010
Cited alongside, same era.
Searching for objects driven by context
B. Alexe, N. Heess, Y. W. Teh, and V. Ferrari · 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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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
Closest in time.
Marc’Aurelio Ranzato · 2014
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