Explainable and Interpretable Models in Co...

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Beschreibung

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 performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.

This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:

· Evaluation and Generalization in Interpretable Machine Learning

· Explanation Methods in Deep Learning

· Learning Functional Causal Models with Generative Neural Networks

· Learning Interpreatable Rules for Multi-Label Classification

· Structuring Neural Networks for More Explainable Predictions

· Generating Post Hoc Rationales of Deep Visual Classification Decisions

· Ensembling Visual Explanations

· Explainable Deep Driving by Visualizing Causal Attention

· Interdisciplinary Perspective on Algorithmic Job Candidate Search

· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

· Inherent Explainability Pattern Theory-based Video Event Interpretations

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.

This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:

· Evaluation and Generalization in Interpretable Machine Learning

· Explanation Methods in Deep Learning

· Learning Functional Causal Models with Generative Neural Networks

· Learning Interpreatable Rules for Multi-Label Classification

· Structuring Neural Networks for More Explainable Predictions

· Generating Post Hoc Rationales of Deep Visual Classification Decisions

· Ensembling Visual Explanations

· Explainable Deep Driving by Visualizing Causal Attention

· Interdisciplinary Perspective on Algorithmic Job Candidate Search

· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

· Inherent Explainability Pattern Theory-based Video Event Interpretations

Über den Autor

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Zusammenfassung

Presents a snapshot of explainable and interpretable models in the context of computer vision and machine learning

Covers fundamental topics to serve as a reference for newcomers to the field

Offers successful methodologies, with applications of interest to the machine learning and computer vision communities

Inhaltsverzeichnis

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.

Details

Erscheinungsjahr:	2019
Fachbereich:	Anwendungs-Software
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Bundle
Reihe:	The Springer Series on Challenges in Machine Learning
Inhalt:	1 Buch 1 MP3, Download oder Online
ISBN-13:	9783319981307
ISBN-10:	3319981307
Sprache:	Englisch
Herstellernummer:	978-3-319-98130-7
Ausstattung / Beilage:	Book + eBook
Einband:	Gebunden
Autor:	Escalante, Hugo Jair Escalera, Sergio Guyon, Isabelle Baró, Xavier Güçlütürk, Yagmur Güçlü, Umut van Gerven, Marcel
Redaktion:	Escalante, Hugo Jair Escalera, Sergio Guyon, Isabelle Baró, Xavier Güçlütürk, Yagmur Güçlü, Umut Gerven, Marcel van
Herausgeber:	Hugo Jair Escalante/Sergio Escalera/Isabelle Guyon et al
Hersteller:	Springer-Verlag GmbH
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Abbildungen:	Bibliographie
Maße:	241 x 161 x 22 mm
Von/Mit:	Hugo Jair Escalante (u. a.)
Erscheinungsdatum:	16.01.2019
Gewicht:	0,673 kg