Towards learning to explain with concept bottleneck models: mitigating information leakage

7 November 2022

Papers citing "Towards learning to explain with concept bottleneck models: mitigating information leakage"

5 / 5 papers shown

Title
If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole Pietro Barbiero Francesco Giannini Andrea Passerini Stefano Teso Emanuele Marconato 137 0 0 28 Apr 2025
Leakage and Interpretability in Concept-Based Models Enrico Parisini Tapabrata Chakraborti Chris Harbron Ben D. MacArthur Christopher R. S. Banerji 40 0 0 18 Apr 2025
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions J. Lockhart Daniele Magazzeni Manuela Veloso 17 4 0 21 Nov 2022
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015