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ORCID4th CD-MAKE 2020: Dublin, Ireland
- Andreas Holzinger
, Peter Kieseberg, A Min Tjoa, Edgar R. Weippl:
Machine Learning and Knowledge Extraction - 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25-28, 2020, Proceedings. Lecture Notes in Computer Science 12279, Springer 2020, ISBN 978-3-030-57320-1 - Luca Longo, Randy Goebel, Freddy Lécué, Peter Kieseberg, Andreas Holzinger:
Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions. 1-16 - Luke Merrick, Ankur Taly:
The Explanation Game: Explaining Machine Learning Models Using Shapley Values. 17-38 - Andrea Campagner, Federico Cabitza:
Back to the Feature: A Neural-Symbolic Perspective on Explainable AI. 39-55 - Xiaoxiao Li, João Saúde:
Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification. 57-76 - Erika Puiutta, Eric M. S. P. Veith:
Explainable Reinforcement Learning: A Survey. 77-95 - Grah Simon, Thouvenot Vincent:
A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance. 97-115 - Annabelle Redelmeier, Martin Jullum, Kjersti Aas:
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. 117-137 - Lukas Felsberger, Andrea Apollonio, Thomas Cartier-Michaud, Andreas Müller, Benjamin Todd, Dieter Kranzlmüller:
Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case. 139-158 - Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso:
eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters. 159-172 - Judita Rokosná, Frantisek Babic, Ljiljana Trtica-Majnaric, Ludmila Pusztová:
Cooperation Between Data Analysts and Medical Experts: A Case Study. 173-190 - Fabrizio Nunnari, Chirag Bhuvaneshwara, Abraham Obinwanne Ezema, Daniel Sonntag:
A Study on the Fusion of Pixels and Patient Metadata in CNN-Based Classification of Skin Lesion Images. 191-208 - David Schneeberger, Karl Stöger, Andreas Holzinger:
The European Legal Framework for Medical AI. 209-226 - Parfait Bemarisika, André Totohasina:
An Efficient Method for Mining Informative Association Rules in Knowledge Extraction. 227-247 - Sanjay Sekar Samuel, Nik Nailah Binti Abdullah, Anil Raj:
Interpretation of SVM Using Data Mining Technique to Extract Syllogistic Rules - Exploring the Notion of Explainable AI in Diagnosing CAD. 249-266 - Jiawen Lyn, Sen Yan:
Non-local Second-Order Attention Network for Single Image Super Resolution. 267-279 - Andreas Theissler, Simon Vollert, Patrick Benz, Laurentius Antonius Meerhoff, Marc Fernandes:
ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers. 281-300 - Martin Teuffenbach, Ewa Piatkowska, Paul Smith:
Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints. 301-320 - Douglas Cirqueira, Dietmar Nedbal, Markus Helfert, Marija Bezbradica:
Scenario-Based Requirements Elicitation for User-Centric Explainable AI - A Case in Fraud Detection. 321-341 - Franz Mayr, Ramiro Visca, Sergio Yovine:
On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks. 343-363 - William Coleman, Charlie Cullen, Ming Yan, Sarah Jane Delany:
Active Learning for Auditory Hierarchy. 365-384 - Vukosi Marivate, Tshephisho Sefara:
Improving Short Text Classification Through Global Augmentation Methods. 385-399 - Lars Patrick Hillebrand, David Biesner, Christian Bauckhage, Rafet Sifa:
Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM. 401-422 - Rhys Agombar, Max Lübbering, Rafet Sifa:
A Clustering Backed Deep Learning Approach for Document Layout Analysis. 423-430 - Philipp Schmidt, Felix Bießmann:
Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias. 431-449 - Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobieczky, Werner Zellinger, Bernhard Moser:
Applying AI in Practice: Key Challenges and Lessons Learned. 451-471 - Padraig Corcoran:
Function Space Pooling for Graph Convolutional Networks. 473-483 - Marco Antonio Pinto-Orellana, Hugo Lewi Hammer:
Analysis of Optical Brain Signals Using Connectivity Graph Networks. 485-497 - Anna Saranti, Behnam Taraghi, Martin Ebner, Andreas Holzinger:
Property-Based Testing for Parameter Learning of Probabilistic Graphical Models. 499-515 - Anna Karanika, Panagiotis Oikonomou, Kostas Kolomvatsos, Christos Anagnostopoulos:
An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge. 517-534 - Amin Anjomshoaa, Edward Curry:
Inter-space Machine Learning in Smart Environments. 535-549
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