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Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems.
Visual object recognition
I. Biederman · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
Y. LeCun and C. Cortes · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Multi-objective optimization using evolutionary algorithms
K. Deb · 2001
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
K. Stanley and R. Miikkulainen · 2002
Earlier work this paper cites.
A taxonomy for artificial embryogeny
K. O. Stanley and R. Miikkulainen · 2003
Earlier work this paper cites.
Learning multiple layers of representation
G. E. Hinton · 2007
Earlier work this paper cites.
Principles of modularity, regularity, and hierarchy for scalable systems
H. Lipson · 2007
Earlier work this paper cites.
Compositional pattern producing networks: A novel abstraction of development
K. O. Stanley · 2007
Earlier work this paper cites.
Bio-inspired artificial intelligence: theories, methods, and technologies
D. Floreano and C. Mattiussi · 2008
Earlier work this paper cites.
Picbreeder: evolving pictures collaboratively online
J. Secretan, N. Beato, D. B. D Ambrosio, A. Rodriguez, A. Campbell, and K. O. Stanley · 2008
Earlier work this paper cites.
Learning deep architectures for ai
Y. Bengio · 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
Cited alongside, same era.
Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
Cited alongside, same era.
Sferes v2: Evolvin’in the multi-core world
J.-B. Mouret and S. Doncieux · 2010
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Cited alongside, same era.
On the performance of indirect encoding across the continuum of regularity
J. Clune, K. Stanley, R. Pennock, and C. Ofria · 2011
Cited alongside, same era.
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng · 2011
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Later among the works it cites.
Deep generative stochastic networks trainable by backprop
Y. Bengio, E. Thibodeau-Laufer, G. Alain, and J. Yosinski · 2014
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Robots that can adapt like natural animals
A. Cully, J. Clune, and J.-B. Mouret · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Automated evolution of interesting images
J. E. Auerbach · 2012
Cited alongside, same era.
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
G. E. Dahl, D. Yu, L. Deng, and A. Acero · 2012
Cited alongside, same era.
Imagenet large scale visual recognition competition 2012 (ilsvrc2012), 2012
J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Cited alongside, same era.
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Introducing the innovation engine: Automated creativity and improved stochastic optimization via deep learning
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