2 papers
cs.CV2024
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
cs.LG2023
Can we Agree? On the Rashōmon Effect and the Reliability of Post-Hoc Explainable AI
Clement Poiret, Antoine Grigis, Justin Thomas +1
The Rashōmon effect poses challenges for deriving reliable knowledge from machine learning models. This study examined the influence of sample size on explanations from models in a…