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An Interactive Introduction to Model-Agnostic Meta-Learning
Zitatschlüssel Mueller20210
Autor Müller, L. and Ploner, M. and Goerttler, T. and Obermayer, K.
Jahr 2021
Journal Workshop on Visualization for AI Explainability at IEEE VIS
Zusammenfassung In this article, we give an interactive introduction to model-agnostic meta-learning (MAML), a well-establish method in the area of meta-learning. Meta-learning is a research field that attempts to equip conventional machine learning architectures with the power to gain meta-knowledge about a range of tasks to solve problems like the one above on a human level of accuracy.
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