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Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 1958
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Computational limitations of small-depth circuits
Håstad, J.: · 1987
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The cascade-correlation learning architecture
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On the power of small-depth threshold circuits
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Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., Frasconi, P.: · 1994
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Learning multiple layers of features from tiny images (2009)
Krizhevsky, A., Hinton, G.: · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., Bengio, Y.: · 2010
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Why does unsupervised pre-training help deep learning?
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
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Torch7: A matlab-like environment for machine learning
Collobert, R., Kavukcuoglu, K., Farabet, C.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: · 2013
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Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: · 2013
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Lin, M., Chen, Q., Yan, S.: · 2013
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
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92.45% on cifar-10 in torch (2015)
Zagoruyko, S.: · 2015
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Recurrent convolutional neural network for object recognition
Liang, M., Hu, X.: · 2015
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On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., Hinton, G.: · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: · 2014
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Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: · 2014
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Graham, B.: · 2014
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Learning activation functions to improve deep neural networks
Agostinelli, F., Hoffman, M., Sadowski, P., Baldi, P.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Ali, M., Adams, R.P., et al.: · 2015
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Training very deep networks
Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
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Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Lee, C.Y., Gallagher, P.W., Tu, Z.: · 2015
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Gradual dropin of layers to train very deep neural networks
Smith, L.N., Hand, E.M., Doster, T.: · 2016
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Training and investigating residual nets (2016)
Gross, S., Wilber, M.: · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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