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Artificial intelligence and machine learning 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, November 10-12, 2021, Revised selected papers /

This book contains a selection of the best papers of the 33rd Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2021, held in Esch-sur-Alzette, Luxembourg, in November 2021. The 14 papers presented in this volume were carefully reviewed and selected from 46 regular submissions. They ad...

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Corporate Authors: Benelux Conference on Artificial Intelligence Esch-sur-Alzette, Luxembourg)
Other Authors: Benelux Conference on Artificial Intelligence, Leiva, Luis A. (Computer scientist), Pruski, Cédric, Markovich, Réka, Najjar, Amro, Schommer, Christoph, SpringerLink (Online service)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2022.
Physical Description: 1 online resource (x, 255 pages) : illustrations (some color).
Series: Communications in computer and information science ; 1530.
Subjects:
Artificial intelligence -- Congresses.
Machine learning -- Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Annotating Data
  • Active Learning for Reducing Labeling Effort in Text Classification Tasks
  • 1 Introduction
  • 2 Related Work
  • 3 Methods
  • 3.1 Active Learning
  • 3.2 Model Architecture
  • 3.3 Query Functions
  • 3.4 Heuristics
  • 3.5 Experimental Setup
  • 4 Results
  • 4.1 Active Learning
  • 4.2 Query-Pool Size
  • 4.3 Heuristics
  • 5 Discussion
  • A.1 RET Algorithm Computational Cost Analysis
  • A.2 Algorithms
  • References
  • Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training.
  • 1 Introduction
  • 2 Related Work
  • 2.1 Supervised Learning for Segmentation
  • 2.2 Weakly-Supervised Semantic Segmentation
  • 2.3 Unsupervised and Weakly-Supervised Monocular Free Space Segmentation
  • 2.4 Training Strategies for Weakly-Supervised Segmentation
  • 3 Methodology
  • 3.1 Data Augmentation
  • 3.2 Recursive Training
  • 4 Experimental Setup
  • 4.1 Dataset
  • 4.2 Evaluation Metrics
  • 4.3 Network Architectures
  • 4.4 Training Procedure
  • 4.5 Use of Ground Truth Data
  • 5 Results
  • 5.1 Fully-Supervised Results
  • 5.2 Unsupervised and Weakly-Supervised Baselines.
  • 5.3 Data Augmentation and Recursive Training
  • 5.4 Limits of Recursive Training
  • 5.5 Qualitative Results
  • 6 Conclusion
  • References
  • Self-labeling of Fully Mediating Representations by Graph Alignment
  • 1 Introduction
  • 2 Related Work
  • 3 Self-labeling of Fully Mediating Representations
  • 3.1 Graph Alignment
  • 3.2 Method
  • 4 Experiments
  • 5 Conclusion
  • A Appendix
  • A.1 Architecture Summary of Graph Recognition Tool
  • A.2 Training Details for Graph Recognition Tool
  • A.3 Computational Cost per Rich-Labeling Iteration
  • A.4 Examples of Cases Where Graph Alignment Fails.
  • 3 Proposed Method
  • 3.1 Adversarial Domain Adaptation for Object Detection
  • 4 Implementation Details
  • 5 Evaluation
  • 5.1 Datasets
  • 5.2 Experiments
  • 6 Conclusion
  • References
  • Explaining Outcomes
  • Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
  • Abstract
  • 1 Introduction
  • 2 Theoretical Background
  • 3 Research Method
  • 3.1 Use Cases
  • 3.2 Data Collection
  • 3.3 Data Analysis
  • 4 Results
  • 4.1 Consumer Credit
  • 4.2 Credit Risk Management
  • 4.3 Anti-money Laundering (AML)
  • 4.4 General
  • 5 Discussion and Conclusions.

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