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Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers.
An nˆ5/2 algorithm for maximum matchings in bipartite graphs
Hopcroft, John E and Karp, Richard M · 1973
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Combinatorial optimization: networks and matroids
Lawler, Eugene L · 1976
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Regression shrinkage and selection via the lasso
Tibshirani, Robert · 1996
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Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
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The difficulty of training deep architectures and the effect of unsupervised pre-training
Erhan, Dumitru, Manzagol, Pierre-Antoine, Bengio, Yoshua, Bengio, Samy, and Vincent, Pascal · 2009
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Kernel analysis of deep networks
Montavon, Grégoire, Braun, Mikio L, and Müller, Klaus-Robert · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoff · 2012
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Understanding deep architectures using a recursive convolutional network
Eigen, David, Rolfe, Jason, Fergus, Rob, and LeCun, Yann · 2013
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Neyshabur, Behnam and Panigrahy, Rina · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, Karen, Vedaldi, Andrea, and Zisserman, Andrew · 2013
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Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian J., and Fergus, Rob · 2013
Cited alongside, same era.
Provable bounds for learning some deep representations
Understanding deep image representations by inverting them
Mahendran, Aravindh and Vedaldi, Andrea · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, Anh, Yosinski, Jason, and Clune, Jeff · 2014
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Why does deep learning work?-a perspective from group theory
Paul, Arnab and Venkatasubramanian, Suresh · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Visualizing and understanding convolutional networks
Zeiler, Matthew D and Fergus, Rob · 2014
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Arora, Sanjeev, Bhaskara, Aditya, Ge, Rong, and Ma, Tengyu · 2014
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Dauphin, Yann, Pascanu, Razvan, Gülçehre, Çaglar, Cho, Kyunghyun, Ganguli, Surya, and Bengio, Yoshua · 2014
Cited alongside, same era.
Explaining and Harnessing Adversarial Examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
Cited alongside, same era.
Object detectors emerge in deep scene cnns
Zhou, Bolei, Khosla, Aditya, Lapedriza, Àgata, Oliva, Aude, and Torralba, Antonio · 2014
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Understanding image representations by measuring their equivariance and equivalence
Lenc, Karel and Vedaldi, Andrea · 2015
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Understanding neural networks through deep visualization
Yosinski, Jason, Clune, Jeff, Nguyen, Anh, Fuchs, Thomas, and Lipson, Hod · 2015
Closest in time.