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View Code? Open in Web Editor NEWA curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
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avinashtgoje rohanchikorde metaresearchclub digitalleanvidhya a-parida12 ravithejaburugu developerscience fendaq yushu-liu abdelpakey trungthanhtran dntai tfknight pengpenglove allensmile alaricforbes awesome-archive nickolatesla369 ianxhu tomrobbinson caoxu915683474 aiyi2099 vasgaowei juingzhou aaaeeee windfivefloor renly rex-hou matthew0717 astoncpou bruceshuyu shaonannan shivam-chandhok maduxof zhongbineden qianruw elenagilbert1 hzjai0624 ryutian xuchi999 khronosplus cyc790 mlipc xjtushujun lirycoac tuvuumass tonylv mr-zhang-sir michaelfan01 qianrenjian kangzi deepchatterjeevns baldrlector zstbackcourt alex-star mars-wei ilovestudying yipeng5 wh-forker berther wuxuemin hunterhawk wangkanger chisyliu etsangsplk mdjabc tianhaofu zyynnn spratik998 yanzhi66 lostbirdx semchan joanshen0508 lilin201501 aiyodiulehuner pkucss mdrw maofengggg lemingguo kazzastic taoxiaolin kaiychen charles-kang ligangzheng sero8139 nooralahzadeh stefanxinhong zeyuxiao1997 wanxnch vivienzou1 jason08 musemamba liyang328 ztf-ucas cfh3c doddaiah noah-yu srinivasgutta7 donghaozhang afcarlawesome-meta-learning's Issues
missing this paper for meta RL
some suggestions
Interesting Papers:
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Metz et al. (2020)
M2SGD: Learning to Learn Important Weights
Kuo et al. (2020)
Meta-Learning in Neural Networks: A Survey
Hospedales et al., (2020)
Hyper-Meta Reinforcement Learning with Sparse Reward
Hua et al., (2020)
Meta-Curvature
Park et al., (2020)
Learning to Learn via Self-Critique
Antoniou et al., (2019)
La-MAML: Look-ahead Meta Learning for Continual Learning
Gupta et al. (2020)
Lectures:
https://www.youtube.com/watch?v=CRHKgOYXVe8
Useful libraries:
Learn2Learn - https://github.com/learnables/learn2learn
Higher - https://github.com/facebookresearch/higher
pytorch-meta - https://github.com/tristandeleu/pytorch-meta
cheers!
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