70 citations · 130 across the 41 of their papers we have counts for
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cs.CV2019★ 4 cited
A psychophysics approach for quantitative comparison of interpretable computer vision models
Felix Biessmann, Dionysius Irza Refiano
The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the…
cs.LG2019★ 70 cited
Quantifying Interpretability and Trust in Machine Learning Systems
Philipp Schmidt, Felix Biessmann
Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML ar…