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Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks.
A value for n-person games
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Optimal brain damage
\NAT@biblabelnum · 1990
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The MNIST database of handwritten digits
\NAT@biblabelnum · 1998
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Paths and consistency in additive cost sharing
\NAT@biblabelnum · 2004
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Polynomial calculation of the Shapley value based on sampling
\NAT@biblabelnum · 2009
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Learning multiple layers of features from tiny images
\NAT@biblabelnum · 2009
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An Efficient Explanation of Individual Classifications using Game Theory
\NAT@biblabelnum · 2010
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Caltech-UCSD Birds 200
\NAT@biblabelnum · 2010
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Do Deep Nets Really Need to be Deep?
\NAT@biblabelnum · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
\NAT@biblabelnum · 2014
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Compressing deep convolutional networks using vector quantization
\NAT@biblabelnum · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
\NAT@biblabelnum · 2014
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Very deep convolutional networks for large-scale image recognition
\NAT@biblabelnum · 2014
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Compressing neural networks with the hashing trick
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Learning both weights and connections for efficient neural network
\NAT@biblabelnum · 2015
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Distilling the knowledge in a neural network
\NAT@biblabelnum · 2015
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
\NAT@biblabelnum · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
\NAT@biblabelnum · 2016
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Training sparse neural networks
\NAT@biblabelnum · 2017
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Axiomatic Attribution for Deep Networks
\NAT@biblabelnum · 2017
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Layer-compensated pruning for resource-constrained convolutional neural networks
\NAT@biblabelnum · 2018
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Amc: Automl for model compression and acceleration on mobile devices
\NAT@biblabelnum · 2018
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Learning Sparse Neural Networks through L0 Regularization
\NAT@biblabelnum · 2018
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Gradient-based attribution methods
\NAT@biblabelnum · 2019
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size
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Fast convnets using group-wise brain damage
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Not just a black box: Learning important features through propagating activation differences
\NAT@biblabelnum · 2016
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Channel Pruning for Accelerating Very Deep Neural Networks
\NAT@biblabelnum · 2017
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Pruning filters for efficient convnets
\NAT@biblabelnum · 2017
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Learning efficient convolutional networks through network slimming
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ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
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Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
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Snip: Single-shot network pruning based on connection sensitivity
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Studying the Plasticity in Deep Convolutional Neural Networks Using Random Pruning
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Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
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Deconstructing lottery tickets: Zeros, signs, and the supermask
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Picking Winning Tickets Before Training by Preserving Gradient Flow
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