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Explaining Deep Classification of Time-Series Data with Learned Prototypes
The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or “prototypes...
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| Veröffentlicht in: | CEUR Workshop Proc |
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| Hauptverfasser: | , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8050893/ https://ncbi.nlm.nih.gov/pubmed/33867901 |
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