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Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer.
Foundations of optimal control theory
E. B. Lee and L. Markus · 1967
Earlier work this paper cites.
Resilience and stability of ecological systems
C. S. Holling · 1973
Earlier work this paper cites.
Feedback as an individual resource: Personal strategies of creating information
S. J. Ashford and L. L. Cummings · 1983
Earlier work this paper cites.
Stacked generalization
D. H. Wolpert · 1992
Earlier work this paper cites.
Learning and development in neural networks: The importance of starting small
J. L. Elman · 1993
Earlier work this paper cites.
Application of the recurrent multilayer perceptron in modeling complex process dynamics
A. G. Parlos, K. T. Chong, and A. F. Atiya · 1994
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Cortical feedback improves discrimination between figure and background by v1, v2 and v3 neurons
J. Hupé, A. James, B. Payne, S. Lomber, P. Girard, and J. Bullier · 1998
Earlier work this paper cites.
Modeling the environment: an introduction to system dynamics models of environmental systems
F. A. Ford · 1999
Earlier work this paper cites.
Hierarchical bayesian inference in the visual cortex
T. S. Lee and D. Mumford · 2003
Earlier work this paper cites.
Minimum bayes-risk decoding for statistical machine translation
S. Kumar and W. Byrne · 2004
Earlier work this paper cites.
Brain states: top-down influences in sensory processing
C. D. Gilbert and M. Sigman · 2007
Earlier work this paper cites.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
Earlier work this paper cites.
Auto-context and its application to high-level vision tasks
Z. Tu · 2008
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
D. Koller and N. Friedman · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Flexible shaping: How learning in small steps helps
K. A. Krueger and P. Dayan · 2009
Earlier work this paper cites.
Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
Earlier work this paper cites.
Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area v4 to it
N. C. Rust and J. J. DiCarlo · 2010
Earlier work this paper cites.
Taxonomic classification for web-based videos
Y. Song, M. Zhao, J. Yagnik, and X. Wu · 2010
Earlier work this paper cites.
Structured prediction cascades
D. J. Weiss and B. Taskar · 2010
Earlier work this paper cites.
Parsing natural scenes and natural language with recursive neural networks
R. Socher, C. C. Lin, C. Manning, and A. Y. Ng · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Convolutional-recursive deep learning for 3d object classification
R. Socher, B. Huval, B. P. Bath, C. D. Manning, and A. Y. Ng · 2012
Cited alongside, same era.
The limits of feedforward vision: Recurrent processing promotes robust object recognition when objects are degraded
D. Wyatte, T. Curran, and R. O’Reilly · 2012
Cited alongside, same era.
3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
Cited alongside, same era.
2d human pose estimation: New benchmark and state of the art analysis
Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2015
Later among the works it cites.
Iterative instance segmentation
K. Li, B. Hariharan, and J. Malik · 2015
Later among the works it cites.
Recurrent convolutional neural network for object recognition
M. Liang and X. Hu · 2015
Later among the works it cites.
Bilinear cnn models for fine-grained visual recognition
T.-Y. Lin, A. RoyChowdhury, and S. Maji · 2015
Later among the works it cites.
Training a feedback loop for hand pose estimation
M. Oberweger, P. Wohlhart, and V. Lepetit · 2015
Later among the works it cites.
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M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Resolving human object recognition in space and time
R. M. Cichy, D. Pantazis, and A. Oliva · 2014
Cited alongside, same era.
Large-scale object classification using label relation graphs
J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, S. Bengio, Y. Li, H. Neven, and H. Adam · 2014
Cited alongside, same era.
Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, et al · 2014
Cited alongside, same era.
Recurrent convolutional neural networks for scene labeling
P. H. Pinheiro and R. Collobert · 2014
Cited alongside, same era.
Pose machines: Articulated pose estimation via inference machines
V. Ramakrishna, D. Munoz, M. Hebert, J. A. Bagnell, and Y. Sheikh · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Later among the works it cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Later among the works it cites.
Efficient object localization using convolutional networks
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler · 2015
Later among the works it cites.
Hyper-class augmented and regularized deep learning for fine-grained image classification
S. Xie, T. Yang, X. Wang, and Y. Lin · 2015
Later among the works it cites.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio · 2015
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V. Belagiannis and A. Zisserman · 2016
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Identity mappings in deep residual networks
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Architectural complexity measures of recurrent neural networks
S. Zhang, Y. Wu, T. Che, Z. Lin, R. Memisevic, R. Salakhutdinov, and Y. Bengio · 2016
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