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Deconstructing lottery tickets: Zeros, signs, and the supermask
Zhou, H., Lan, J., Liu, R., and Yosinski, J. (2019) · 1905
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Wang, Y., Zhang, X., Xie, L., Zhou, J., Su, H., Zhang, B., and Hu, X. (2019) · 1909
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What’s Hidden in a Randomly Weighted Neural Network?
Ramanujan, V., Wortsman, M., Kembhavi, A., Farhadi, A., and Rastegari, M. (2020) · 1911
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Mozer, M. C. and Smolensky, P. (1989) · 1989
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Universal approximation using feed-forward networks with nonsigmoid hidden layer activation functions
Stinchombe, M. (1989) · 1989
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Optimal Brain Damage
LeCun, Y., Denker, J. S., and Solla, S. A. (1990) · 1990
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Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R. (1993) · 1993
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Second order derivatives for network pruning: Optimal Brain Surgeon
Hassibi, B. and Stork, D. G. (1993) · 1993
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Fast Pruning Using Principal Components
Levin, A. U., Leen, T. K., and Moody, J. E. (1994) · 1994
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Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results
Scarselli, F. and Tsoi, A. C. (1998) · 1998
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Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient
Pensia, A., Rajput, S., Nagle, A., Vishwakarma, H., and Papailiopoulos, D. (2020) · 2006
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J.-P. (2015) · 2015
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Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016) · 2016
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P. (2016) · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016) · 2016
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Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., and Li, H. (2016) · 2016
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Quantized convolutional neural networks for mobile devices
Wu, J., Leng, C., Wang, Y., Hu, Q., and Cheng, J. (2016) · 2016
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The universal approximation power of finite-width deep relu networks
Perekrestenko, D., Grohs, P., Elbrächter, D., and Bölcskei, H. (2018) · 2018
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A Survey of Model Compression and Acceleration for Deep Neural Networks
Cheng, Y., Wang, D., Zhou, P., and Zhang, T. (2019) · 2019
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Universal function approximation by deep neural nets with bounded width and relu activations
Hanin, B. (2019) · 2019
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A review of binarized neural networks
Simons, T. and Lee, D.-J. (2019) · 2019
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What is the state of neural network pruning?
Blalock, D., Gonzalez Ortiz, J. J., Frankle, J., and Guttag, J. (2020) · 2020
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Zhu, C., Han, S., Mao, H., and Dally, W. J. (2016) · 2016
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J. (2017) · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y. (2017) · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S. (2017) · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Frankle, J. and Carbin, M. (2018) · 2018
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Amc: Automl for model compression and acceleration on mobile devices
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., and Han, S. (2018) · 2018
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Approximation by combinations of relu and squared relu ridge functions with ℓ 1 \ell^{1} and ℓ 0 \ell^{0} controls
Klusowski, J. M. and Barron, A. R. (2018) · 2018
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Model compression and hardware acceleration for neural networks: A comprehensive survey
Deng, L., Li, G., Han, S., Shi, L., and Xie, Y. (2020) · 2020
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Linear mode connectivity and the lottery ticket hypothesis
Frankle, J., Dziugaite, G. K., Roy, D., and Carbin, M. (2020) · 2020
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Universal approximation with deep narrow networks
Kidger, P. and Lyons, T. (2020) · 2020
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Proving the lottery ticket hypothesis: Pruning is all you need
Malach, E., Yehudai, G., Shalev-Schwartz, S., and Shamir, O. (2020) · 2020
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Logarithmic pruning is all you need
Orseau, L., Hutter, M., and Rivasplata, O. (2020) · 2020
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Diffenderfer, J. and Kailkhura, B. (2021) · 2021
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