A Framework to Learn with Interpretation

A Framework to Learn with Interpretation

19 October 2020

Pavlo Mozharovskyi

Florence dÁlché-Buc

Papers citing "A Framework to Learn with Interpretation"

10 / 10 papers shown

Title
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi A. Newson Florence dÁlché-Buc SSL 63 1 0 01 Jul 2024
Prototypical Self-Explainable Models Without Re-training Srishti Gautam Ahcène Boubekki Marina M.-C. Höhne Michael C. Kampffmeyer 28 2 0 13 Dec 2023
Interpretability-Aware Vision Transformer Yao Qiang Chengyin Li Prashant Khanduri D. Zhu ViT 82 7 0 14 Sep 2023
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 34 1 0 18 May 2023
Posthoc Interpretation via Quantization Francesco Paissan Cem Subakan Mirco Ravanelli MQ 16 6 0 22 Mar 2023
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Florence dÁlché-Buc G. Richard 16 22 0 23 Feb 2022
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 24 49 0 27 Aug 2021
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
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,684 0 28 Feb 2017