maml-finn-2017-optimizes-initialization-parameters

IN premise — summaries/2026/08/24/wiki-Meta-learning_computer_science.md

Created 2026-08-25T02:58:49+00:00

MAML (Finn et al., 2017) is an optimization-based meta-learning method that trains initial parameters so that a small number of gradient steps on a new task yield good generalization.

Summary

MAML is a method for choosing the best starting point for a model's parameters so that it can adapt to a brand-new task with only a handful of training examples. In practice, this means a system doesn't need to be retrained from scratch every time it faces a new problem; it just needs a few quick adjustments to get reliable performance.