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.
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Showing 1 to 20 of 145 for “"Model training"”.
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Data curation for foundation model training
… focused on algorithmic improvements, with better training methods driving innovation. Given that the amount of data available for training these models was often limited, research aimed on improving the way these relatively small amounts of data could be used. More recently, this focus has shifted …
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Optimizing end-to-end machine learning pipelines for model training
… and apply machine learning algorithms to train models on the preprocessed data. Existing systems can execute such end-to-end training pipelines. However, they face unique challenges in their applicability to large scale data. In particular, current approaches either rely on in-memory execution …
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Impact of Person-Environment-Occupation Model Training on Teacher Transition Problem-Solving
… there is currently no universal framework or model that is applied to the transition planning process for students with disabilities, other than the transition mandates set forth by the Individuals with Disabilities Education Act (IDEA, 2004). This often results in educators picking transition …
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Developing a training program for civil service employees : a model training program for the Socialist People's Libyan Arab Jamahiriya
The purpose of this study was to develop a training program for civil service employees in the Socialist People's Libyan Arab Jamahiriya. The intent was to investigate the effectiveness of recent training programs and to discover the training needs upon which a new and possibly stronger training …
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Metagradient Descent: Differentiating Large-Scale Training
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for …
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Data Centric Defenses for Privacy Attacks
… sensitive information about the data used in model training. These attacks called privacy attacks, exploit the model training process. Contemporary defense techniques make alterations to the training algorithm. Such defenses are computationally expensive, cause a noticeable privacy-utility …
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On Neural Network Pruning’s Effect on Generalization
… frequently observe that pruning improves model generalization. A longstanding hypothesis attributes such improvement to model size reduction. However, recent studies on over-parameterization characterize a new model size regime, in which larger models achieve better generalization. A …
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Adversarial Resilient and Privacy Preserving Deep learning
… intelligence, ranging from data poisoning and model inversion during the training phase and adversarial evasion attacks during model inference phase, aiming to cause the well-trained model to misbehave randomly or purposefully. This dissertation research addresses these problems with dual …
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Digital Humanities: a bridge between computer vision and study of art
… research in four parts: “Data Preparation,” “Model Training,” “Evaluation and Optimization,” and “Analysis and Interpretation,” each part including an introduction to basic knowledge, the application of technology (experiments), and reflections on deep learning. Chapter One, Data Preparation, …
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Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
… performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). The …
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Multimedia Big Data Analytics and Fusion for Data Science
… spatio-temporal deep feature extraction, and model training optimization strategy. First, a hierarchical graph fusion network is presented to capture the inter-modality correlations among modalities. The network hierarchy models the modality-wise combinations with gradually increased …
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N-ary Cross-sentence Relation Extraction: From Supervised to Unsupervised Learning
… in the absence of sufficient labeled data for training. This work aims to overcome these limitations by developing n-ary cross-sentence relation extraction methods for both supervised and unsupervised settings. Our work has three main goals and contributions: (1) two unsupervised binary …
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Scalable predictive modeling for spatiotemporally evolving phenomena
… and traffic management, among others. Models can be used to understand and inform decision-making in these settings. There has been a growth in both mechanistic and physics-informed methods to model phenomena. A challenge in such models is the need for extensive parametrization and …
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Design and development of reusable feature-based next-generation embedded software
… demands like mobility and machine learning (ML) model training. This research focuses on identifying the reusable features through static analysis from legacy embedded software to improve code reuse for faster development and create a feature model for understanding features and their …
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Towards a transparency-based, value-sensitive design solution for bias in self-driving cars: An ethical violation assessment and risk analysis framework on consumer-held values
… concerning patterns in machine learning (ML) model training data labeling and gathering practices that can be easily identified and potentially mitigated.</p> <p>Conclusions: Aligning AI systems like SDCs with human values is achievable but requires careful attention. Identifying and …
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Examining the Impact of a Model of Human Occupation Training on Occupational Therapy Students’ Professional Reasoning
… of a six-hour intensive occupation-centered model training in the Model of Human Occupation paired with an experiential learning experience on occupational therapy students’ perceived professional reasoning. A concurrent mixed methods design, with a pretest-posttest control group design, and …
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Federated self-supervised learning
… advantages beyond privacy-preserving training, including robust distributed representation learning, enhanced scalability, and resilience to noisy data. Despite its potential, research on SSL within the context of FL remains scarce. This thesis endeavours to bridge this research gap by …
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Bootstrapping fully-automatic temporal fetal brain segmentation in volumetric MRI time series
We present a method for bootstrapping training data for the task of segmenting fetal brains in volumetric MRI time series data. Temporal analysis of MRI images requires accurate segmentation across frames, despite large amounts of unpredictable motion. We use the predicted segmentations of a …
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Transfer learning and robustness for natural language processing
… of deep learning, state-of-the-art NLP models have already achieved human-level performance in various large benchmark datasets, such as SQuAD, SNLI, and RACE. However, when these strong models are deployed to real-world applications, they often show poor generalization capability in two …
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Exploration of small enrollment speaker verification on handheld devices
… devices under the context of extremely limited training data. Although speaker verification technology is an area of great promise for security applications, the implementation of such a system on handheld devices presents its own unique challenges arising from the highly mobile nature of the …
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