@inproceedings{f34b12720a88406ab46791c0bb26aba1,
title = "M2SGD: Learning to learn important weights",
abstract = "Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their handcrafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71\% weights for ResNet20s.We release our codes at https://github.com/Nic5472K/CLVISION2020-CVPR-M2SGD.",
author = "Kuo, \{Nicholas I.Hsien\} and Mehrtash Harandi and Nicolas Fourrier and Christian Walder and Gabriela Ferraro and Hanna Suominen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 ; Conference date: 14-06-2020 Through 19-06-2020",
year = "2020",
month = jul,
day = "28",
doi = "10.1109/CVPRW50498.2020.00126",
language = "English",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "957--964",
booktitle = "Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020",
address = "United States",
}