Explainable and Interpretable Models in Computer Vision and Machine Learning
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like per...
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | , , , , , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2018.
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| Έκδοση: | 1st ed. 2018. |
| Σειρά: | The Springer Series on Challenges in Machine Learning,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 1 Considerations for Evaluation and Generalization in Interpretable Machine Learning
- 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
- 3 Learning Functional Causal Models with Generative Neural Networks
- 4 Learning Interpretable Rules for Multi-label Classification
- 5 Structuring Neural Networks for More Explainable Predictions
- 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions
- 7 Ensembling Visual Explanations
- 8 Explainable Deep Driving by Visualizing Causal Action
- 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
- 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
- 11 On the Inherent Explainability of Pattern Theory-based Video Event Interpretations. .
