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Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields.
A logical calculus of the ideas immanent in nervous activity
McCulloch, W.S. and Pitts, W · 1943
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffne, P · 1998
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Improved method of handwritten digit recognition tested on mnist database
Kussul, E. and Baidyk, T · 2004
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Pattern recognition. machine learning, 2006
Bishop, C.M · 2006
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Collobert, R. and Weston, J · 2008
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A. and Hinton, G · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng., A.Y · 2011
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On optimization methods for deep learning
Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q.V., and Ng., A.Y · 2011
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Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., and Bengio, Y · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., and Kingsbury, B · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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How does the brain solve visual object recognition?
Zoccolan, J. J. DiCarloand D. and Rust, N.C · 2012
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Maxout networks
Goodfellow, I. J., D.Warde-Farley, Mirza, M., Courville, A. C., and Bengio, Y · 2013
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Lin, M., Chen, Q., and Yan, S · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Improving deep neural networks with probabilistic maxout units
Springenberg, J.T. and Riedmiller, M · 2013
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Discriminative transfer learning with tree-based priors
Srivastava, N. and Salakhutdinov, R.R · 2013
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On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G.E., and Hinton, G.E · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., and Fergus, R · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, M.D. and Fergus, R · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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The stanford corenlp natural language processing toolkit
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Deeply-supervised nets
Lee, C.Y., Xie, S., Gallagher, P.W., Zhang, Z., and Tu, Z · 2015
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Recurrent convolutional neural network for object recognition
Liang, M. and Hu, X · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., and Wierstra, D · 2015
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Mishkin, D. and Matas, J · 2015
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Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., and McClosky, D · 2014
Cited alongside, same era.
Recurrent convolutional neural networks for scene labeling
Pinheiro, P.H. and Collobert, R · 2014
Cited alongside, same era.
Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., and Bengio, Y · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., and M.Riedmiller · 2014
Cited alongside, same era.
Deep networks with internal selective attention through feedback connections
Stollenga, M.F., Masci, J., Gomez, F., and Schmidhuber, J · 2014
Cited alongside, same era.
Deep captioning with multimodal recurrent neural networks (m-rnn)
Xu, J. Maoand W., Yang, Y., Wang, J., Huang, Z., and Yuille, A · 2014
Cited alongside, same era.
Deep learning in neural networks: An overview
Schmidhuber, J · 2015
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Training very deep networks
Srivastava, R. K., Greff, K., and Schmidhuber, J · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A · 2015
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Transferring rich feature hierarchies for robust visual tracking
Wang, N., Li, S., Gupta, A., and Yeun, D. Y · 2015
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Keras. https://github.com/fchollet/keras, 2016
Chollet, F · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer · 2016
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Improving stochastic gradient descent with feedback
Koushik, J. and Hayashi, H · 2016
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Refining architectures of deep convolutional neural networks
Shankar, S., Robertson, D., Ioannou, Y., Criminisi, A., and Cipolla, R · 2016
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Do deep convolutional nets really need to be deep and convolutional?
Urban, G., Geras, K.J., Kahou, S.E., Aslan, O., Wang, S., Caruana, R., and Richardson, M · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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