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Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision.
An introduction to frames and Riesz bases
Christensen, O · 2003
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-j., Li, K., and Fei-Fei, L · 2009
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Measuring invariances in deep networks
Goodfellow, I., Lee, H., Le, Q. V., Saxe, A., and Ng, A · 2009
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What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y · 2009
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G · 2010
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The importance of encoding versus training with sparse coding and vector quantization
Coates, A. and Ng, A · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, l., and Hinton, G · 2012
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Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
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Signal recovery from pooling representations
Bruna, J., Szlam, A., and LeCun, Y · 2013
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Maxout networks
Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y · 2013
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Network in network
Lin, M., Chen, Q., and Yan, S · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A., Hannun, A. Y., and Ng, A · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., LeCun, Y., and Fergus, R · 2013
Cited alongside, same era.
Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, M. D. and Fergus, R · 2013
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 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., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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Spectral representations for convolutional neural networks
Rippel, O., Snoek, J., and Adams, R · 2015
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Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M. M. A., and Adams, R · 2015
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Training very deep networks
Srivastava, R., 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., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Empirical evaluation of rectified activations in convolutional network
Xu, B., Wang, N., Chen, T., and Li, M · 2015
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Cited alongside, same era.
Inverting convolutional networks with convolutional networks
Dosovitskiy, A. and Brox, T · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W. J · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Convolutional neural network with biologically inspired on/off relu
Kim, J., Kim, S., and Lee, M · 2015
Cited alongside, same era.
Recurrent convolutional neural network for object recognition
Liang, M. and Hu, X · 2015
Cited alongside, same era.
Deep fried convnets
Yang, Z., Moczulski, M., Denil, M., de Freitas, N., Smola, A., Song, L., and Wang, Z · 2015
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Torch blog
Zagoruyko, S · 2015
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Stacked what-where auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., and Lecun, Y · 2015
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Max-min convolutional neural networks for image classfication
Blot, M., Cord, M., and Thome, N · 2016
Closest in time.
Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Lee, C.-y., Gallagher, P. W., and Tu, Z · 2016
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