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: Faculty for Languages and Literatures

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.

• Item Type: Theses (Dissertation, LMU Munich)
• 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
• Subjects: 000 Computers, Information and General Reference; 000 Computers, Information and General Reference > 004 Data processing computer science; 400 Language; 400 Language > 410 Linguistics
• Faculties: Faculty for Languages and Literatures
• Language: English
• Date of oral examination: 10. July 2024
• 1. Referee: Schmid, Helmut
• Signature of the printed copy: 0001/UMC 30588