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The search for efficient, sparse deep neural network models is most prominently performed by pruning: training a dense, overparameterized network and removing parameters, usually via following a manually-crafted heuristic.
Optimal brain damage
LeCun, Y., J. S. Denker, S. A. Solla · 1990
Earlier work this paper cites.
Introduction to Numerical Continuation Methods
Allgower, E. L., K. Georg · 2003
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., N. Léonard, A. Courville · 2013
Earlier work this paper cites.
Learning both weights and connections for efficient neural networks
Han, S., J. Pool, J. Tran, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., A. Zisserman · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
Russakovsky, O., J. Deng, H. Su, et al · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P., J. Ba · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Han, S., H. Mao, W. J. Dally · 2016
Earlier work this paper cites.
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 1MB model size
Iandola, F. N., M. W. Moskewicz, K. Ashraf, et al · 2016
Earlier work this paper cites.
Training sparse neural networks
Srinivas, S., A. Subramanya, R. Venkatesh Babu · 2016
Earlier work this paper cites.
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He, K., X. Zhang, S. Ren, et al · 2016
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