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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Overview of attention for book
Cover of 'Explainable AI: Interpreting, Explaining and Visualizing Deep Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Towards Explainable Artificial Intelligence
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    Chapter 2 Transparency: Motivations and Challenges
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    Chapter 3 Interpretability in Intelligent Systems – A New Concept?
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    Chapter 4 Understanding Neural Networks via Feature Visualization: A Survey
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    Chapter 5 Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
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    Chapter 6 Unsupervised Discrete Representation Learning
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    Chapter 7 Towards Reverse-Engineering Black-Box Neural Networks
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    Chapter 8 Explanations for Attributing Deep Neural Network Predictions
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    Chapter 9 Gradient-Based Attribution Methods
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    Chapter 10 Layer-Wise Relevance Propagation: An Overview
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    Chapter 11 Explaining and Interpreting LSTMs
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    Chapter 12 Comparing the Interpretability of Deep Networks via Network Dissection
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    Chapter 13 Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison
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    Chapter 14 The (Un)reliability of Saliency Methods
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    Chapter 15 Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation
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    Chapter 16 Understanding Patch-Based Learning of Video Data by Explaining Predictions
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    Chapter 17 Quantum-Chemical Insights from Interpretable Atomistic Neural Networks
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    Chapter 18 Interpretable Deep Learning in Drug Discovery
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    Chapter 19 NeuralHydrology – Interpreting LSTMs in Hydrology
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    Chapter 20 Feature Fallacy: Complications with Interpreting Linear Decoding Weights in fMRI
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    Chapter 21 Current Advances in Neural Decoding
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    Chapter 22 Software and Application Patterns for Explanation Methods
Overall attention for this book and its chapters
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Mentioned by

news
15 news outlets
blogs
5 blogs
policy
1 policy source
twitter
73 tweeters
patent
1 patent
facebook
1 Facebook page
wikipedia
2 Wikipedia pages
reddit
1 Redditor

Citations

dimensions_citation
374 Dimensions

Readers on

mendeley
299 Mendeley
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Title
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-28954-6
ISBNs
978-3-03-028953-9, 978-3-03-028954-6
Editors

Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Muller

Twitter Demographics

The data shown below were collected from the profiles of 73 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 299 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 299 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 26%
Student > Master 58 19%
Researcher 40 13%
Student > Bachelor 32 11%
Professor 10 3%
Other 34 11%
Unknown 46 15%
Readers by discipline Count As %
Computer Science 105 35%
Engineering 47 16%
Neuroscience 9 3%
Earth and Planetary Sciences 8 3%
Business, Management and Accounting 7 2%
Other 62 21%
Unknown 61 20%