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Explainable and Interpretable Models in Computer Vision and Machine Learning

Overview of attention for book
Cover of 'Explainable and Interpretable Models in Computer Vision and Machine Learning'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Considerations for Evaluation and Generalization in Interpretable Machine Learning
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    Chapter 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
  4. Altmetric Badge
    Chapter 3 Learning Functional Causal Models with Generative Neural Networks
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    Chapter 4 Learning Interpretable Rules for Multi-Label Classification
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    Chapter 5 Structuring Neural Networks for More Explainable Predictions
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    Chapter 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions
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    Chapter 7 Ensembling Visual Explanations
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    Chapter 8 Explainable Deep Driving by Visualizing Causal Attention
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    Chapter 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
  11. Altmetric Badge
    Chapter 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
  12. Altmetric Badge
    Chapter 11 On the Inherent Explainability of Pattern Theory-Based Video Event Interpretations
Attention for Chapter 2: Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
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Citations

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Chapter title
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Chapter number 2
Book title
Explainable and Interpretable Models in Computer Vision and Machine Learning
Published by
Springer, Cham, November 2018
DOI 10.1007/978-3-319-98131-4_2
Book ISBNs
978-3-31-998130-7, 978-3-31-998131-4
Authors

Gabriëlle Ras, Marcel van Gerven, Pim Haselager, Ras, Gabriëlle, van Gerven, Marcel, Haselager, Pim

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 283 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 283 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 23%
Student > Master 43 15%
Student > Bachelor 22 8%
Researcher 18 6%
Student > Doctoral Student 13 5%
Other 28 10%
Unknown 93 33%
Readers by discipline Count As %
Computer Science 106 37%
Engineering 26 9%
Business, Management and Accounting 9 3%
Mathematics 7 2%
Social Sciences 6 2%
Other 29 10%
Unknown 100 35%

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