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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

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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
Attention for Chapter 18: Interpretable Deep Learning in Drug Discovery
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Chapter title
Interpretable Deep Learning in Drug Discovery
Chapter number 18
Book title
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-28954-6_18
Book ISBNs
978-3-03-028953-9, 978-3-03-028954-6
Authors

Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner

Mendeley readers

The data shown below were compiled from readership statistics for 216 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 216 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 21%
Researcher 38 18%
Student > Master 35 16%
Student > Bachelor 17 8%
Student > Postgraduate 7 3%
Other 27 13%
Unknown 46 21%
Readers by discipline Count As %
Computer Science 62 29%
Chemistry 22 10%
Engineering 19 9%
Biochemistry, Genetics and Molecular Biology 16 7%
Agricultural and Biological Sciences 7 3%
Other 40 19%
Unknown 50 23%