5 citations · 14 across the 20 of their papers we have counts for
3 papers · 1 filter
Signatures to help interpretability of anomalies
Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov +8
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, an…
Inferring Properties of Graph Neural Networks
Dat Nguyen, Hieu M. Vu, Cong-Thanh Le +4
We propose GNNInfer, the first automatic property inference technique for GNNs. To tackle the challenge of varying input structures in GNNs, GNNInfer first identifies a set of repr…
Challenging common interpretability assumptions in feature attribution explanations
Jonathan Dinu, Jeffrey Bigham, J. Zico Kolter
As machine learning and algorithmic decision making systems are increasingly being leveraged in high-stakes human-in-the-loop settings, there is a pressing need to understand the r…