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State-of-the-art neural networks are getting deeper and wider.
Advances in neural information processing systems 1
Hanson, S.J., Pratt, L.: · 1989
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Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B., Stork, D.G.: · 1993
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
Graves, A., Schmidhuber, J.: · 2005
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Accelerating large-scale convolutional neural networks with parallel graphics multiprocessors
Scherer, D., Schulz, H., Behnke, S.: · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Predicting parameters in deep learning
Denil, M., Shakibi, B., Dinh, L., Ranzato, M., de Freitas, N.: · 2013
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Lin, M., Chen, Q., Yan, S.: · 2013
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Deepface: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Learning both weights and connections for efficient neural networks
Han, S., Pool, J., Tran, J., Dally, W.J.: · 2015
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Han, S., Mao, H., Dally, W.J.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Sequence to sequence-video to text
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
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Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 1mb model size
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: · 2016
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