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There exists a plethora of techniques for inducing structured sparsity in parametric models during the optimization process, with the final goal of resource-efficient inference.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Karen Simonyan and Andrew Zisserman · 2015
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Suraj Srinivas and R Venkatesh Babu · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
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MorphNet: Fast & simple resource-constrained structure learning of deep networks
Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi · 2018
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P. Kingma · 2018
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An experimental analysis of the power consumption of convolutional neural networks for keyword spotting
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