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The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights.
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
Cited alongside, same era.
Bayesian Compression for Deep Learning
C. Louizos, K. Ullrich, and M. Welling · 2017
Cited alongside, same era.
Jonathan Frankle and Michael Carbin · 2019
Closest in time.
The lottery ticket hypothesis at scale
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2019
Closest in time.