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In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Backpropagation applied to handwritten zip code recognition
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Long short-term memory
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Unconstrained on-line handwriting recognition with recurrent neural networks
A. Graves, M. Liwicki, H. Bunke, J. Schmidhuber, and S. Fernández · 2008
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80 million tiny images: A large dataset for non-parametric object and scene recognition
A. Torralba, R. Fergus, and W. T. Freeman · 2008
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Offline handwriting recognition with multidimensional recurrent neural networks
A. Graves and J. Schmidhuber · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Theano: a CPU and GPU math expression compiler
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio · 2010
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Deep big simple neural nets excel on handwritten digit recognition
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Learning recurrent neural networks with Hessian-free optimization
J. Martens and I. Sutskever · 2011
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Reading digits in natural images with unsupervised feature learning
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Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition
O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn · 2012
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Theano: new features and speed improvements
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Statistical Language Models based on Neural Networks
T. Mikolov · 2012
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Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Trainable COSFIRE filters for keypoint detection and pattern recognition
G. Azzopardi and N. Petkov · 2013
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Maxout networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Generating sequences with recurrent neural networks
A. Graves · 2013
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On the difficulty of training recurrent neural networks
R. Pascanu, T. Mikolov, and Y. Bengio · 2013
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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Convolutional kernel networks
J. Mairal, P. Koniusz, Z. Harchaoui, and C. Schmid · 2014
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How to construct deep recurrent neural networks
R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Deep convolutional neural networks for lvcsr
T. N. Sainath, A.-r. Mohamed, B. Kingsbury, and B. Ramabhadran · 2013
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Improving deep neural networks with probabilistic maxout units
J. T. Springenberg and M. A. Riedmiller · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. D. Zeiler, S. Zhang, Y. LeCun, and R. Fergus · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
M. D. Zeiler and R. Fergus · 2013
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio · 2014
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End-to-end continuous speech recognition using attention-based recurrent nn: First results
J. Chorowski, D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 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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Combining time-and frequency-domain convolution in convolutional neural network-based phone recognition
L. Tóth · 2014
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Show and tell: a neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Gated feedback recurrent neural networks
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2015
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Convolutional, long short-term memory, fully connected deep neural networks
T. N. Sainath, O. Vinyals, A. Senior, and H. Sak · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. L. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio · 2015
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Video description generation incorporating spatio-temporal features and a soft-attention mechanism
L. Yao, A. Torabi, K. Cho, N. Ballas, C. Pal, H. Larochelle, and A. Courville · 2015
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