The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning
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Published By Springer International Publishing

9783319981307, 9783319981314

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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_2 ◽

2018 ◽

pp. 19-36 ◽

Cited By ~ 22

Author(s):

Gabriëlle Ras ◽

Marcel van Gerven ◽

Pim Haselager

Keyword(s):

Deep Learning

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On the Inherent Explainability of Pattern Theory-Based Video Event Interpretations

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_11 ◽

2018 ◽

pp. 277-299

Author(s):

Sathyanarayanan N. Aakur ◽

Fillipe D. M. de Souza ◽

Sudeep Sarkar

Keyword(s):

Pattern Theory ◽

Video Event

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Explainable Deep Driving by Visualizing Causal Attention

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_8 ◽

2018 ◽

pp. 173-193

Author(s):

Jinkyu Kim ◽

John Canny

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Ensembling Visual Explanations

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_7 ◽

2018 ◽

pp. 155-172 ◽

Cited By ~ 1

Author(s):

Nazneen Fatema Rajani ◽

Raymond J. Mooney

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Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_9 ◽

2018 ◽

pp. 197-253 ◽

Cited By ~ 4

Author(s):

Cynthia C. S. Liem ◽

Markus Langer ◽

Andrew Demetriou ◽

Annemarie M. F. Hiemstra ◽

Achmadnoer Sukma Wicaksana ◽

...

Keyword(s):

Machine Learning

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Learning Interpretable Rules for Multi-Label Classification

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_4 ◽

2018 ◽

pp. 81-113 ◽

Cited By ~ 4

Author(s):

Eneldo Loza Mencía ◽

Johannes Fürnkranz ◽

Eyke Hüllermeier ◽

Michael Rapp

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Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_10 ◽

2018 ◽

pp. 255-275 ◽

Cited By ~ 1

Author(s):

Heysem Kaya ◽

Albert Ali Salah

Keyword(s):

Personality Trait ◽

Job Interview ◽

Trait Analysis

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Generating Post-Hoc Rationales of Deep Visual Classification Decisions

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_6 ◽

2018 ◽

pp. 135-154 ◽

Cited By ~ 1

Author(s):

Zeynep Akata ◽

Lisa Anne Hendricks ◽

Stephan Alaniz ◽

Trevor Darrell

Keyword(s):

Visual Classification ◽

Post Hoc

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Considerations for Evaluation and Generalization in Interpretable Machine Learning

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_1 ◽

2018 ◽

pp. 3-17 ◽

Cited By ~ 10

Author(s):

Finale Doshi-Velez ◽

Been Kim

Keyword(s):

Machine Learning ◽

Interpretable Machine Learning

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Structuring Neural Networks for More Explainable Predictions

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning ◽

10.1007/978-3-319-98131-4_5 ◽

2018 ◽

pp. 115-131 ◽

Cited By ~ 3

Author(s):

Laura Rieger ◽

Pattarawat Chormai ◽

Grégoire Montavon ◽

Lars Kai Hansen ◽

Klaus-Robert Müller

Keyword(s):

Neural Networks

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The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine LearningLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

On the Inherent Explainability of Pattern Theory-Based Video Event Interpretations

Explainable Deep Driving by Visualizing Causal Attention

Ensembling Visual Explanations

Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

Learning Interpretable Rules for Multi-Label Classification

Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

Generating Post-Hoc Rationales of Deep Visual Classification Decisions

Considerations for Evaluation and Generalization in Interpretable Machine Learning

Structuring Neural Networks for More Explainable Predictions

The Springer Series on Challenges in Machine Learning - Explainable and Interpretable Models in Computer Vision and Machine Learning
Latest Publications