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An Interactive Introduction to Model-Agnostic Meta-Learning
Citation key Mueller20210
Author Müller, L. and Ploner, M. and Goerttler, T. and Obermayer, K.
Year 2021
Journal Workshop on Visualization for AI Explainability at IEEE VIS
Abstract 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.
Bibtex Type of Publication Selected:structured selected:publications selected:main selected:quantify
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