Interpretable by Design: Learning Predictors by Composing Interpretable Queries

3 July 2022

Papers citing "Interpretable by Design: Learning Predictors by Composing Interpretable Queries"

6 / 6 papers shown

Title
3VL: Using Trees to Improve Vision-Language Models' Interpretability Nir Yellinek Leonid Karlinsky Raja Giryes CoGe VLM 44 4 0 28 Dec 2023
Variational Information Pursuit for Interpretable Predictions Aditya Chattopadhyay Kwan Ho Ryan Chan B. Haeffele D. Geman René Vidal DRL 8 10 0 06 Feb 2023
Natural Language Descriptions of Deep Visual Features Evan Hernandez Sarah Schwettmann David Bau Teona Bagashvili Antonio Torralba Jacob Andreas MILM 194 92 0 26 Jan 2022
A Rate-Distortion Framework for Explaining Black-box Model Decisions Stefan Kolek Duc Anh Nguyen Ron Levie Joan Bruna Gitta Kutyniok 16 14 0 12 Oct 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 120 293 0 17 Oct 2019
Active Testing for Face Detection and Localization Raphael Sznitman Bruno Michel Jedynak CVBM VLM 57 80 0 27 Mar 2010