5 citations · 11 across the 3 of their papers we have counts for
3 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.LG2020★ 5 cited
Attention-Based Learning on Molecular Ensembles
Kangway V. Chuang, Michael J. Keiser
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual scr…
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…