Search Results for author: Julian Blackwell

Found 1 papers, 1 papers with code

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

1 code implementation5 Feb 2024 Vinitra Swamy, Syrielle Montariol, Julian Blackwell, Jibril Frej, Martin Jaggi, Tanja Käser

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i. e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance.

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