Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Introduces DIP that encapsulates sample mixing inside the hypothesis class to reduce Rademacher complexity and improve generalization over standard Mixup.
citing papers explorer
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Importance Estimation for Neural Network Pruning
Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.
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Data Interpolating Prediction: Alternative Interpretation of Mixup
Introduces DIP that encapsulates sample mixing inside the hypothesis class to reduce Rademacher complexity and improve generalization over standard Mixup.