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That neural networks may be pruned to high sparsities and retain high accuracy is well established.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
Caffe con troll: Shallow ideas to speed up deep learning
Hadjis, S., Abuzaid, F., Zhang, C., and Ré, C · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Tiny imagenet challenge, 2017
Wu, J., Zhang, Q., and Xu, G · 2017
Earlier work this paper cites.
Deep learning for pedestrians: backpropagation in cnns
Boué, L · 2018
Earlier work this paper cites.
Measuring the intrinsic dimension of objective landscapes
Li, C., Farkhoor, H., Liu, R., and Yosinski, J · 2018
Earlier work this paper cites.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., and Liotta, A · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
Xiao, L., Bahri, Y., Sohl-Dickstein, J., Schoenholz, S., and Pennington, J · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M · 2019
Cited alongside, same era.
The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 2019
Cited alongside, same era.
Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2020
Later among the works it cites.
Linear mode connectivity and the lottery ticket hypothesis
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2020
Later among the works it cites.
Pruning algorithms to accelerate convolutional neural networks for edge applications: A survey
Liu, J., Tripathi, S., Kurup, U., and Shah, M · 2020
Later among the works it cites.
Proving the lottery ticket hypothesis: Pruning is all you need
Malach, E., Yehudai, G., Shalev-Schwartz, S., and Shamir, O · 2020
Later among the works it cites.
Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L., and Ganguli, S · 2020
Later among the works it cites.
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Snip: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P · 2019
Cited alongside, same era.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Mostafa, H. and Wang, X · 2019
Cited alongside, same era.
Residual learning without normalization via better initialization
Zhang, H., Dauphin, Y. N., and Ma, T · 2019
Cited alongside, same era.
What is the state of neural network pruning?
Blalock, D., Ortiz, J. J. G., Frankle, J., and Guttag, J · 2020
Cited alongside, same era.
Wang, C., Zhang, G., and Grosse, R · 2020
Later among the works it cites.
Progressive skeletonization: Trimming more fat from a network at initialization
de Jorge, P., Sanyal, A., Behl, H., Torr, P., Rogez, G., and Dokania, P. K · 2021
Closest in time.
Pruning neural networks at initialization: Why are we missing the mark?
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M · 2021
Closest in time.