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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 4: Learning Interpretable Rules for Multi-Label Classification
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