Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 74 for “"data scarcity"”.
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Adaptive deep learning under data scarcity
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Harnessing data priors to mitigate 3D data scarcity
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
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Scalable Representation Learning: On Data-scarcity, Uncertainty and Symmetry
… include the necessity for extensive labeled datasets, the lack of trustworthiness due to model overconfidence, and difficulties in generalizing to new, unseen data. In this thesis, our primary goal is to tackle these issues by introducing novel tools and methods that augment traditional deep …
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Overcoming Data Scarcity in Deep Learning of Scientific Problems
Data-driven approaches such as machine learning have been increasingly applied to the natural sciences, e.g. for property prediction and optimization or material discovery. An essential criteria to ensure the success of such methods is the need for extensive amounts of labeled data, making it …
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Generalizing Under Data Scarcity. Enhancing the representation capability from few samples.
… lack large, diverse, and reliably labeled datasets. This challenge is particularly evident in domains where data acquisition is costly, error-prone, or inherently scarce, such as industrial inspection, anomaly detection and localization. This thesis investigates how learning systems can be …
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COLLABORATIVE APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION IN DATA SCARCITY SCENARIOS
… train a recognition model over the data collected from a large number of users. However, these solutions usually suffer from numerous issues like scalability, privacy, poor personalization, and scarcity of labeled training data. In this thesis, we will focus on analyzing in deep …
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Advancing semantic modeling: addressing coordination, interpretability, and data scarcity in domain representation
… both short documents and domains with limited data. These challenges limit their usefulness for applications such as search, recommendation, trend analysis, and domain comparison, where interpretability and adaptability are essential. This thesis develops four complementary solutions that …
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MITIGATING DATA SCARCITY CHALLENGES IN MEDICAL IMAGING ANALYSIS:ADVANCED LEARNING APPROACHES WITH EMPHASIS ON HEMOPHILIC ULTRASOUND IMAGES
… systems. However, the lack of labeled training data makes the effective utilization of DL techniques in the medical domain impractical, leading to suboptimal performance in various imaging tasks, such as classification, detection, and segmentation. This thesis investigates the application of …
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ENHANCING BUILDING FAULT DETECTION, DIAGNOSTICS, AND PROGNOSTICS THROUGH A HYBRID PHYSICS-INFORMED MODELING FRAMEWORK FOR OVERCOMING QUANTITY AND TEMPORAL DATA SCARCITY
… (FDDP) in buildings is severely hindered by the scarcity of real-world fault data. This thesis proposes a hybrid physics-informed modeling framework to overcome both data quantity scarcity and temporal sparsity. First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By …
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Toward building more accessible large language models: A preliminary empirical study on data scarcity in knowledge distillation and algorithm complexity in alignment
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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The Dark Side of the Eye: Towards Robust Deep Learning Models for Uveal Melanoma Screening
… can handle large quantities of high-dimensional data, such as images. However, two main challenges need to be addressed. First, the state-of-the-art research related to UM screening is fragmented. Few solutions are available in the literature, with most studies focusing on analyzing tissue images …
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Composing Foundation Models for Decision Making
… model for decision-making is hindered by the scarcity and expense of collecting paired visual, language, and action data. To address this challenge, this thesis proposes a scalable alternative: a compositional model architecture that leverages separately trained expert models specializing in …
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Deep Transfer Learning for Macroscale Defect Detection in Semiconductor Manufacturing
… in detail, and a novel approach to overcoming data scarcity through the creation of synthetic data is deployed. The binary classifier model achieves an out-of-distribution area under curve (AUC) of 0.909 for detecting hotspot defects. Detection for other classes of central defects is also …
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Tabular Machine Learning on Small-Size and High-Dimensional Data
… on small-size and high-dimensional tabular datasets. Tabular data – tables where each row represents an individual record and each column represents features – is ubiquitous in critical fields such as medicine, scientific research and finance. However, these areas often face data scarcity …
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Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents
… significantly when subjected to severe data scarcity and the presence of heterogeneous agents. In this work, we propose a model-based offline RL method to approach this setting. Using all available data from the various agents, we construct personalized simulators for each individual …
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Design and development of an LLM-based framework for synthetic data generation
The increasing demand for high-quality datasets in fields such as healthcare, finance, and cybersecurity is hindered by challenges such as data scarcity, privacy concerns, and regulatory restrictions. This thesis introduces a novel framework for generating synthetic data using fine-tuned Large …
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A Transformer for scATAC-scRNA Translation
… theory missed by scRNA-seq. However, scATAC-seq data is highdimensional and noisy, aspects which when compounded with data scarcity present challenges for modeling on even seemingly-simple downstream tasks such as cell-type prediction. As such, researchers may benefit from access to a large …
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Towards Data Efficiency and Controllable Representations for Deep Learning in Resource-Constrained Domains
… faces two fundamental challenges: label scarcity and data scarcity. Label scarcity stems from the high cost of expert annotation, the scarcity of domain experts, and the infeasibility of crowdsourcing, particularly in complex tasks requiring specialised knowledge. In parallel, data …
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NEURO-SYMBOLIC AI APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION
… often limited by their inherent opacity and the scarcity of labeled training data. Fortunately, common sense and domain knowledge about activity execution can improve purely data-driven approaches. Indeed, in the general machine learning community, Neuro-Symbolic AI (NeSy) methods are emerging to …
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Large-scale Process-based Urban Hydrological Modeling Framework
… and urban components and effectively overcomes data scarcity and computational limitations common in large-scale urban modeling. It utilizes graph theory and various land datasets to derive BUSNs, providing a practical solution to the BUSN data scarcity issue. The algorithm demonstrated its …
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