floodsung/Meta-Learning-Papers
Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
Curated collection spanning foundational meta-learning theory through contemporary approaches including memory-augmented networks, gradient-based optimization (MAML), Siamese architectures, and reinforcement learning applications. Organized chronologically from seminal works on learning-to-learn principles to recent innovations in neural architecture search and learned optimizers. Covers both supervised few-shot learning paradigms and meta-RL formulations across vision and control domains.
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