Name
Abstract | ||
|---|---|---|
Kandinsky Patterns are mathematically describable, simple, self-contained, hence controllable test datasets for the development, validation and training of explainability in artificial intelligence (AI) and machine learning (ML). |
Year | Venue | DocType |
|---|---|---|
2020 | ERCIM NEWS | Journal |
Volume | Issue | ISSN |
2020 | 120 | 0926-4981 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Holzinger | 1 | 2886 | 253.75 |
| Peter Kieseberg | 2 | 187 | 29.39 |
| Heimo Müller | 3 | 0 | 0.34 |