Meta-learning (computer science): Difference between revisions

Revision as of 18:37, 11 December 2019

Revision as of 23:10, 3 January 2020

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====LSTM Meta-Learner====

LSTM-based meta-learner is to learn the exact [[optimization algorithm]] used to train another learner [[neural network]] [[classification rule|classifier]]{{dn|date=November 2019}} in the few-shot regime. The parametrization allows it to learn appropriate parameter updates specifically for the [[scenario]] where a set amount of updates will be made, while also learning a general initialization of the learner (classifier) network that allows for quick [[convergence]]{{dn|date=November 2019}} of training.<ref name="paper8">[https://openreview.net/pdf?id=rJY0-Kcll] Sachin Ravi∗and Hugo Larochelle(2017).” Optimization as a model for few-shot learning”. ICLR 2017. Retrieved 3 November, 2019</ref>

====Temporal Discreteness====