4 citations · 6 across the 2 of their papers we have counts for
2 papers
stat.ML2021★ 2 cited
Robust Semantic Interpretability: Revisiting Concept Activation Vectors
Jacob Pfau, Albert T. Young, Jerome Wei +2
Interpretability methods for image classification assess model trustworthiness by attempting to expose whether the model is systematically biased or attending to the same cues as a…
cs.CV2019★ 4 cited
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias
Jacob Pfau, Albert T. Young, Maria L. Wei +1
In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons. In medical imaging, saliency maps h…