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GENERALIZED INNER LOOP META-LEARNING

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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


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


higher: ∂Hello/∂World! This is the dev team for higher, a @PyTorch library by @facebookAI which facilitates the implementation of gradient-based meta-learning algorithms. Code: https://github.com/facebookresearch/higher/ PyPi: https://pypi.org/project/higher/ Docs: https://higher.readthedocs.io/en/latest/

1 replies, 121 likes


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


Miles Brundage: "Generalized Inner Loop Meta-Learning," @egrefen et al.: https://arxiv.org/abs/1910.01727

1 replies, 59 likes


Ethan Rosenthal: Lots of fun things you can do with this, like in the referenced paper (https://arxiv.org/abs/1910.01727) where the authors let the learning rate be a free parameter and then optimize it.

1 replies, 4 likes


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


Content

Found on Oct 07 2019 at https://arxiv.org/pdf/1910.01727.pdf

PDF content of a computer science paper: GENERALIZED INNER LOOP META-LEARNING