Artificial Intelligence and Machine Learning

Name: 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
Brand: Springer
Price: 85.59 EUR
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33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, November 10-12, 2021, Revised Selected Papers

Luis A. Leiva Cédric Pruski Réka Markovich Amro Najjar Christoph Schommer(Herausgeber*in)

Springer (Verlag)

Erschienen am 11. Januar 2022

X, 255 Seiten

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Inhalt

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
References
Recognizing Objects
Task Independent Capsule-Based Agents for Deep Q-Learning
1 Introduction
2 Background
2.1 Capsule Networks
2.2 Deep Reinforcement Learning
3 Related Work
4 Methodology
5 Analysis
5.1 Cumulative Reward and Parameters
5.2 Input State
5.3 Action Space
5.4 Reward
6 Discussion
6.1 Training
6.2 Environment
7 Conclusion
References
Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices
1 Introduction
2 Related Work
2.1 Object Detection
2.2 Domain Adaptation
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
References
The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models
1 Introduction
2 Related Work
3 Causal Discovery Methods
3.1 Notations
3.2 Regression with Subsequent Independence Test (Resit)
3.3 Identification Using Conditional Variances (Uncertainty Scoring)
4 Experimental Setup
5 Experimental Results
5.1 Resit
5.2 Uncertainty Scoring
6 Conclusions
References
A Bayesian Framework for Evaluating Evolutionary Art
1 Introduction
2 Background
2.1 Evaluating Computer-Generated Art
3 The Bayesian Framework
3.1 Art Turing Test
3.2 Bayesian Model Comparison
4 Application
4.1 Tree Representation
4.2 The Mathematical Fitness Function
4.3 Results and Analysis
5 Questionnaire
6 Code Base
7 Discussion
8 Conclusion
References
Understanding Language
Dutch SQuAD and Ensemble Learning for Question Answering from Labour Agreements
1 Introduction
2 Related Work
3 Datasets
3.1 Dutch SQuAD v2.0
3.2 Labour Agreement Dataset
4 Approach
4.1 Fine-Tuning
4.2 Voted BERT
5 Evaluation
5.1 Models
5.2 Evaluation Metrics
6 Results
6.1 Dutch SQuAD
6.2 Labour Agreement Dataset
7 Discussion
7.1 Conclusion
References
Verbalizing but Not Just Verbatim Translations of Ontology Axioms
1 Introduction
2 Related Work
3 Preliminaries and Defintions
4 Proposed Verbalization Approach
5 Semantic-Refinement of Label-Sets
6 Empirical Evaluation
6.1 Results and Discussions
7 Conclusion
References
Transfer Learning and Curriculum Learning in Sokoban
1 Introduction
2 Related Work
3 Experimental Setup
3.1 Neural Network Architecture
3.2 Transfer Approach
4 Experiments
4.1 Transfer Among Related Tasks
4.2 Transfer Among Different Tasks (SL/RL)
4.3 Transfer to Different Appearance
4.4 Visualizing Agent Detection
5 Conclusion and Future Work
References
Reinforcing Decisions
Proximal Policy Optimisation for a Private Equity Recommitment System
1 Introduction
2 Related Works
3 Problem Description
4 Proximal Policy Optimisation
5 Private Equity Recommitment as RL Problem
5.1 Modelling
5.2 Synthetic Cashflows
6 Experimental Setups
7 Experimental Results
8 Conclusion
References
Regular Decision Processes for Grid Worlds
1 Introduction
2 Background
2.1 Markov Decision Processes
2.2 Non-Markovian Decision Processes
2.3 Temporal Logic, Automata and Product MDPs
2.4 Regular Decision Processes: Non-Markovian Dynamics
2.5 Related Work
3 Approach and Software Design
3.1 Compilation: From RDP to MDP
4 Experiments
4.1 Experiment 1: Goal Sparsity
4.2 Experiment 2: Reward Shaping
4.3 Experiment 3: Safety
4.4 Experiment 4: Non-Markovian Transitions
5 Conclusions and Future Work
References
MoveRL: To a Safer Robotic Reinforcement Learning Environment
1 Introduction
2 Notations
3 Related Work
3.1 RL Robotic Environment
3.2 Safe Reinforcement Learning
3.3 Path Planning
4 Contribution
4.1 The Gym Environment
4.2 Observation Space
4.3 Action Space
4.4 Why Do We Need Sequences of Actions?
4.5 Reward Function
4.6 Initial States and Termination
4.7 Safety Guarantee
5 Experiment
5.1 Learning Scenarios
5.2 Learning Algorithm
5.3 Results
6 Conclusion
References
Author Index

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