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Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks.
The mnist database of handwritten digits, 1998
Yann LeCun and Corinna Cortes · 1998
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A discriminatively trained, multiscale, deformable part model
Pedro Felzenszwalb, David McAllester, and Deva Ramanan · 2008
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Learning deep architectures for ai
Yoshua Bengio · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Measuring invariances in deep networks
Ian Goodfellow, Quoc Le, Andrew Saxe, Honglak Lee, and Andrew Y Ng · 2009
Cited alongside, same era.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
Cited alongside, same era.
Building high-level features using large scale unsupervised learning
Quoc V Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S Corrado, Jeff Dean, and Andrew Y Ng · 2011
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey E. Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, and Brian Kingsbury · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton · 2012
Later among the works it cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2013
Closest in time.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Closest in time.
Visualizing and understanding convolutional neural networks
Matthew D Zeiler and Rob Fergus · 2013
Closest in time.
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