Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.¶
Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), 1126-1135.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Metaplasticity
- In machine learning, meta-learning, learning-rate schedulers, and adaptive optimizers maintain per-parameter or per-task adaptation rates that themselves update on history, and continual-learning regularizers raise stiffness on weights important to prior tasks.
This sourceFormalizes learning-to-learn—optimizing the adaptation procedure itself from accumulated task history.
- In machine learning, meta-learning, learning-rate schedulers, and adaptive optimizers maintain per-parameter or per-task adaptation rates that themselves update on history, and continual-learning regularizers raise stiffness on weights important to prior tasks.
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