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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Overview of attention for book
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Springer International Publishing

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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 4: Understanding Neural Networks via Feature Visualization: A Survey
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Chapter title
Understanding Neural Networks via Feature Visualization: A Survey
Chapter number 4
Book title
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-28954-6_4
Book ISBNs
978-3-03-028953-9, 978-3-03-028954-6
Authors

Anh Nguyen, Jason Yosinski, Jeff Clune, Nguyen, Anh, Yosinski, Jason, Clune, Jeff

Timeline

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 245 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 24%
Student > Master 47 19%
Researcher 29 12%
Student > Bachelor 15 6%
Student > Doctoral Student 10 4%
Other 25 10%
Unknown 60 24%
Readers by discipline Count As %
Computer Science 110 45%
Engineering 26 11%
Neuroscience 9 4%
Physics and Astronomy 5 2%
Business, Management and Accounting 3 1%
Other 21 9%
Unknown 71 29%