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Explainable and Interpretable Models in Computer Vision and Machine Learning

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Cover of 'Explainable and Interpretable Models in Computer Vision and Machine Learning'

Table of Contents

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    Book Overview
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    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
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    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
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    Chapter 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
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    Chapter 11 On the Inherent Explainability of Pattern Theory-Based Video Event Interpretations
Attention for Chapter 5: Structuring Neural Networks for More Explainable Predictions
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Citations

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Chapter title
Structuring Neural Networks for More Explainable Predictions
Chapter number 5
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_5
Book ISBNs
978-3-31-998130-7, 978-3-31-998131-4
Authors

Laura Rieger, Pattarawat Chormai, Grégoire Montavon, Lars Kai Hansen, Klaus-Robert Müller

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 18%
Student > Bachelor 2 12%
Student > Doctoral Student 2 12%
Professor > Associate Professor 2 12%
Lecturer 1 6%
Other 4 24%
Unknown 3 18%
Readers by discipline Count As %
Engineering 5 29%
Computer Science 4 24%
Mathematics 1 6%
Energy 1 6%
Physics and Astronomy 1 6%
Other 2 12%
Unknown 3 18%

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