Artificial Intelligence for automated decision-making in error-pattern recognition

Guimerà Cuevas, Felip (2024): Artificial Intelligence for automated decision-making in error-pattern recognition. Dissertation, LMU München: Fakultät für Sprach- und Literaturwissenschaften

Abstract

The demand for integrating Artificial Intelligence (AI) into diverse systems continues to expand rapidly. With growing reliance on AI, there is a constant need to deliver increasingly more dependable and robust solutions. This thesis aims to address common Machine Learning (ML) challenges and offers solutions in the field of automated decision-making and pattern recognition. It explores, presents, and analyzes novel methods and algorithms for Representation Learning, focusing on "Neural Architecture & Data Representation", "Manifold & Embedding Learning", and "Data Integration & Analysis". The contributions of this thesis include: (1) a novel neural architecture based on complex-valued neural networks, (2) a framework for encoding hierarchical one-to-many relationship databases into contextualized numeric data representations, (3) an adaptive and robust feature normalization and pre-processing technique, (4) a method for synthetic data augmentation on hierarchical databases, (5) a contrastive Representation Learning framework for tree structures, (6) extending and generalizing hierarchical Embedding Learning to multiple data views, (7) methods and diverse loss functions for Manifold Representation Learning and Clustering, (8) approaches for automated textual description generation for cluster groups, (9) a quality evaluation metric for clustering under coarse label uncertainty, (10) methods for determining representative textual labels for clustering accurate sensor data with inexact annotations, (11) a Transfer Learning approach to distilling insights from ML models trained on different data sets, (12) a novel approach to Out-of-Distribution Detection and Novelty Detection by leveraging mismatches in model calibration alignment, and (13) a method for representative sampling within quantile windows in data streams of unknown final length.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: Artificial Intelligence (AI), Machine Learning (ML), Automated decision-making, Pattern recognition, Representation Learning, Neural Architecture & Data Representation, Manifold & Embedding Learning, Data Integration & Analysis, Complex-valued neural networks, Hierarchical one-to-many relationship databases
• Themengebiete: 000 Allgemeines, Informatik, Informationswissenschaft ; 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik ; 400 Sprache ; 400 Sprache > 410 Linguistik
• Fakultäten: Fakultät für Sprach- und Literaturwissenschaften
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 10. Juli 2024
• 1. Bericht­erstatter:in: Schmid, Helmut