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Title: GitHub - RAIVNLab/STR: Soft Threshold Weight Reparameterization for Learnable Sparsity · GitHub

Open Graph Title: GitHub - RAIVNLab/STR: Soft Threshold Weight Reparameterization for Learnable Sparsity

X Title: GitHub - RAIVNLab/STR: Soft Threshold Weight Reparameterization for Learnable Sparsity

Description: Soft Threshold Weight Reparameterization for Learnable Sparsity - RAIVNLab/STR

Open Graph Description: Soft Threshold Weight Reparameterization for Learnable Sparsity - RAIVNLab/STR

X Description: Soft Threshold Weight Reparameterization for Learnable Sparsity - RAIVNLab/STR

Opengraph URL: https://github.com/RAIVNLab/STR

X: @github

direct link

Domain: github.com

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trainer.pyhttps://github.com/RAIVNLab/STR/blob/master/trainer.py
READMEhttps://github.com/RAIVNLab/STR
Apache-2.0 licensehttps://github.com/RAIVNLab/STR
Soft Threshold Weight Reparameterization for Learnable Sparsityhttps://arxiv.org/abs/2002.03231
https://github.com/RAIVNLab/STR#soft-threshold-weight-reparameterization-for-learnable-sparsity
Aditya Kusupatihttps://homes.cs.washington.edu/~kusupati/
Vivek Ramanujan*https://vkramanuj.github.io/
Raghav Somani*http://raghavsomani.github.io/
Mitchell Worstsman*https://mitchellnw.github.io/
Prateek Jainhttp://www.prateekjain.org/
Sham Kakadehttps://homes.cs.washington.edu/~sham/
Ali Farhadihttps://homes.cs.washington.edu/~ali/
paperhttps://arxiv.org/abs/2002.03231
hidden-networks repositoryhttps://github.com/allenai/hidden-networks
STRhttps://arxiv.org/abs/2002.03231
DNWhttps://arxiv.org/abs/1906.00586
GMPhttps://arxiv.org/abs/1710.01878
paperhttps://arxiv.org/abs/2002.03231
FastGRNNCellhttp://manikvarma.org/pubs/kusupati18.pdf
EdgeMLhttps://github.com/Microsoft/EdgeML
https://github.com/RAIVNLab/STR#set-up
https://github.com/RAIVNLab/STR#strconv
STRConvhttps://github.com/RAIVNLab/STR/blob/master/utils/conv_type.py#L22
utils/conv_type.pyhttps://github.com/RAIVNLab/STR/blob/master/utils/conv_type.py
DenseConvhttps://github.com/RAIVNLab/STR/blob/master/utils/conv_type.py#L12
https://github.com/RAIVNLab/STR#vanilla-training
ResNet18https://github.com/RAIVNLab/STR/blob/master/models/resnet.py#L156
ResNet50https://github.com/RAIVNLab/STR/blob/master/models/resnet.py#L161
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https://github.com/RAIVNLab/STR#train-dense-models-on-imagenet-1k
STRhttps://arxiv.org/abs/2002.03231
https://github.com/RAIVNLab/STR#train-models-with-str-on-imagenet-1k
paperhttps://arxiv.org/abs/2002.03231
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GMPhttps://arxiv.org/abs/1710.01878
https://github.com/RAIVNLab/STR#train-resnet50-models-with-dnw-and-gmp-on-imagenet-1k
https://github.com/RAIVNLab/STR#models-and-logging
https://github.com/RAIVNLab/STR#convert-str-model-to-dense-model
https://github.com/RAIVNLab/STR#evaluating-models-on-imagenet-1k
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https://github.com/RAIVNLab/STR#sparsity-budget-transfer
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https://github.com/RAIVNLab/STR#pretrained-models
paperhttps://arxiv.org/abs/2002.03231
https://github.com/RAIVNLab/STR#fully-dense-models
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https://github.com/RAIVNLab/STR#str-sparse-models
Aditya Kusupatihttps://homes.cs.washington.edu/~kusupati/
paperhttps://arxiv.org/abs/2002.03231
https://github.com/RAIVNLab/STR#resnet50
STRhttps://drive.google.com/file/d/1ztb0UfsFVJQkE2vj6eNHxnab9MXuLryk/view?usp=sharing
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https://github.com/RAIVNLab/STR#mobilenetv1--
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https://github.com/RAIVNLab/STR#sparsity-budgets
budgetshttps://github.com/RAIVNLab/STR/blob/master/budgets
https://github.com/RAIVNLab/STR#citation
homes.cs.washington.edu/~kusupati/#Kusupati20https://homes.cs.washington.edu/~kusupati/#Kusupati20
sparsity https://github.com/topics/sparsity
cnn https://github.com/topics/cnn
imagenet https://github.com/topics/imagenet
str https://github.com/topics/str
icml https://github.com/topics/icml
efficient-inference https://github.com/topics/efficient-inference
soft-thresholding https://github.com/topics/soft-thresholding
edge-machine-learning https://github.com/topics/edge-machine-learning
sparsity-optimization https://github.com/topics/sparsity-optimization
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learnable-sparsity https://github.com/topics/learnable-sparsity
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soft-threshold-reparameterization https://github.com/topics/soft-threshold-reparameterization
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