Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning

14 September 2023

Papers citing "Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning"

16 / 16 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
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 80 0 0 30 Mar 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens Samuele Bortolotti Emanuele Marconato Paolo Morettin Andrea Passerini Stefano Teso 61 2 0 16 Feb 2025
Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions H. Fokkema T. Erven Sara Magliacane 70 1 0 10 Feb 2025
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go? Jae Hee Lee Georgii Mikriukov Gesina Schwalbe Stefan Wermter D. Wolter 52 2 0 20 Sep 2024
Learning Causal Abstractions of Linear Structural Causal Models Riccardo Massidda Sara Magliacane Davide Bacciu CML 52 2 0 01 Jun 2024
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts Emanuele Marconato Samuele Bortolotti Emile van Krieken Antonio Vergari Andrea Passerini Stefano Teso 41 18 0 19 Feb 2024
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations Atticus Geiger Zhengxuan Wu Christopher Potts Thomas F. Icard Noah D. Goodman CML 75 98 0 05 Mar 2023
GlanceNets: Interpretabile, Leak-proof Concept-based Models Emanuele Marconato Andrea Passerini Stefano Teso 106 64 0 31 May 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 145 185 0 31 May 2022
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations Wolfgang Stammer Marius Memmel P. Schramowski Kristian Kersting 91 26 0 04 Dec 2021
Coherent Hierarchical Multi-Label Classification Networks Eleonora Giunchiglia Thomas Lukasiewicz AILaw 37 96 0 20 Oct 2020
Conditional Gaussian Distribution Learning for Open Set Recognition Xin Sun Zhen Yang Chi Zhang Guohao Peng K. Ling BDL UQCV 155 216 0 19 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 313 0 07 Feb 2020
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
Logic Tensor Networks for Semantic Image Interpretation Ivan Donadello Luciano Serafini Artur Garcez 59 209 0 24 May 2017