Who Cited It

Methods for interpreting and understanding deep neural networks

2017 · Digital Signal Processing · 2,763 citations · 7 from inside this corpus

Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.

Methods for interpreting and understanding deep neural networks (2017)Methods for interpreting and …Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…Learning representations by back-propagating errors (1986)Learning representations by b…Neural networks for pattern recognition (1994)Neural networks for pattern r…"Why Should I Trust You?" (2016)"Why Should I Trust You?"Advances in neural information processing systems 7 (1996)Advances in neural informatio…Domain-Adversarial Training of Neural Networks (2017)Domain-Adversarial Training o…Neural Networks: Tricks of the Trade (2012)Neural Networks: Tricks of th…Proceedings of The 32nd International Conference on Machine Learning (2015)Proceedings of The 32nd Inter…Advances in Neural Information Processing Systems 21 (2009)Advances in Neural Informatio…Intelligible Models for HealthCare (2015)Intelligible Models for Healt…Explaining nonlinear classification decisions with deep Taylor decomposition (2016)Explaining nonlinear classifi…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…Deep learning and process understanding for data-driven Earth system science (2019)Deep learning and process und…A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2020)A Survey on Explainable Artif…Causability and explainability of artificial intelligence in medicine (2019)Causability and explainabilit…Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw… (2023)Explainable Artificial Intell…Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021)Explaining Deep Neural Networ…Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019)Explainable AI: Interpreting,…
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What this paper cites, inside the corpus

PaperYearCited
Dropout: a simple way to prevent neural networks from overfitting201434,236
Learning representations by back-propagating errors198631,688
Neural networks for pattern recognition199418,720
"Why Should I Trust You?"201616,210
Advances in neural information processing systems 7199614,393
Domain-Adversarial Training of Neural Networks20177,702
Neural Networks: Tricks of the Trade20122,159
Proceedings of The 32nd International Conference on Machine Learning20152,041
Advances in Neural Information Processing Systems 2120091,860
Intelligible Models for HealthCare20151,693
Explaining nonlinear classification decisions with deep Taylor decomposition20161,413

What cites it, inside the corpus

PaperYearCited
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
Deep learning and process understanding for data-driven Earth system science20195,475
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI20202,377
Causability and explainability of artificial intelligence in medicine20191,851
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…20231,679
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications20211,424
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning20191,386

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Topics

Explainable Artificial Intelligence (XAI)Computer Science
Neural Networks and ApplicationsComputer Science
Adversarial Robustness in Machine LearningComputer Science

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