@inbook{a70766e98fd0453685fe10f8e193cc19,
title = "A Rate-Distortion Framework for Explaining Black-Box Model Decisions",
abstract = "We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations of the target input signal and applies to any differentiable pre-trained model such as neural networks. Our experiments demonstrate the framework{\textquoteright}s adaptability to diverse data modalities, particularly images, audio, and physical simulations of urban environments.",
author = "Stefan Kolek and Nguyen, {Duc Anh} and Ron Levie and Joan Bruna and Gitta Kutyniok",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).; International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, xxAI 2020, held in Conjunction with ICML 2020 ; Conference date: 18-07-2020 Through 18-07-2020",
year = "2022",
doi = "https://doi.org/10.1007/978-3-031-04083-2_6",
language = "الإنجليزيّة",
isbn = "9783031040825",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "91--115",
editor = "Andreas Holzinger and Randy Goebel and Ruth Fong and Taesup Moon and Klaus-Robert M{\"u}ller and Wojciech Samek",
booktitle = "xxAI@ICML",
address = "ألمانيا",
}