| 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. |
| Typ der Publikation |
Selected:structured selected:publications selected:main selected:quantify |
