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Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets.
Adaptive mixtures of local experts
Jacobs, Robert A., Jordan, Michael I., Nowlan, Steven J., and Hinton, Geoffrey E · 1991
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Semantic hashing
Salakhutdinov, Ruslan and Hinton, Geoffrey · 2008
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Visualizing data using t-SNE
Van der Maaten, Laurens and Hinton, Geoffrey · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Deep sparse rectifier networks
Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011
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The manifold tangent classifier
Rifai, Salah, Dauphin, Yann, Vincent, Pascal, Bengio, Yoshua, and Muller, Xavier · 2011
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ImageNet large scale visual recognition competition 2012 (ILSVRC2012)
Deng, Jia, Berg, Alex, Satheesh, Sanjeev, Hao, Su, Khosla, Aditya, and Li, Fei-Fei · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E., Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
LDAHash: Improved Matching with Smaller Descriptors
Strecha, Christop, Bronstein, Alex M., Bronstein, Michael M., and Fua, Pascal · 2012
Cited alongside, same era.
CloudCV: Large-Scale distributed computer vision as a cloud service
Batra, D., Agrawal, H., Banik, P., Chavali, N., and Alfadda, A · 2013
Cited alongside, same era.
Learning binary codes for high-dimensional data using bilinear projections
Gong, Yunchao, Kumar, Sanjiv, Rowley, Henry A., and Lazebnik, Svetlana · 2013
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Learning binary hash codes for large-scale image search
Grauman, Kristen and Fergus, Rob · 2013
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On the number of response regions of deep feed forward networks with piece-wise linear activations
Pascanu, Razvan, Montufar, Guido, and Bengio, Yoshua · 2013
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Compete to compute
Srivastava, Rupesh K., Masci, Jonathan, Kazerounian, Sohrob, Gomez, Faustino, and Schmidhuber, Jürgen · 2013
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On rectified linear units for speech processing
Zeiler, Matthew D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q. V., Nguyen, P., Senior, A., Vanhoucke, V., and Dean, J · 2013
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Donahue, Jeff, Jia, Yangqing, Vinyals, Oriol, Hoffman, Judy, Zhang, Ning, Tzeng, Eric, and Darrell, Trevor · 2013
Cited alongside, same era.
Maxout networks
Goodfellow, Ian, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua
Cited in the paper.
Pylearn2: a machine learning research library
Goodfellow, Ian J., Warde-Farley, David, Lamblin, Pascal, Dumoulin, Vincent, Mirza, Mehdi, Pascanu, Razvan, Bergstra, James, Bastien, Frédéric, and Bengio, Yoshua
Cited in the paper.
Multimodal Similarity-Preserving Hashing
Masci, Jonathan, Bronstein, Alex M., Bronstein, Michael M., and Schmidhuber, Jürgen
Cited in the paper.
Sparse similarity-preserving hashing
Masci, Jonathan, Bronstein, Alex M., Bronstein, Michael M., Sprechmann, Pablo, and Sapiro, Guillermo
Cited in the paper.
Goodfellow, Ian J., Mirza, Mehdi, Da, Xiao, Courville, Aaron, and Bengio, Yoshua · 2014
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