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GENERALIZED INNER LOOP META-LEARNING

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Oct 07 2019 Edward Grefenstette

Happy to announce our paper on Generalized Inner Loop Meta Learning, aka Gimli (https://arxiv.org/abs/1910.01727), with @brandondamos, @denisyarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, @douwekiela, @kchonyc, and @soumithchintala. THREAD [1/6] https://t.co/oBF53sBeuc
5 replies, 296 likes


Oct 07 2019 Edward Grefenstette

In parallel with this paper, @facebookai has released higher, a library for bypassing limitations to taking higher-order gradients over an optimization process. Library: https://github.com/facebookresearch/higher Docs: https://higher.readthedocs.io Contributions very welcome.
1 replies, 251 likes


Oct 07 2019 Andrei Bursuc

MAML(s) for the masses: a new pytorch library for implementing existing and developing new meta-learning algos https://github.com/facebookresearch/higher The source papers features a pedagogical description of inner loop meta-learning algos
0 replies, 74 likes


Oct 07 2019 Miles Brundage

"Generalized Inner Loop Meta-Learning," @egrefen et al.: https://arxiv.org/abs/1910.01727
1 replies, 59 likes


Dec 31 2019 Edward Grefenstette

To enable this work, and other in this area, we released a library (higher) and described the general form of such meta-learning approaches with colleagues at @facebookai (9/16) https://github.com/facebookresearch/higher https://arxiv.org/abs/1910.01727 https://t.co/0tJ3lGFZW4
1 replies, 3 likes


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