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 278 for “"Learning systems"”.
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Provably reliable machine learning systems
Machine learning systems, which primarily use deep neural networks (DNNs), serve as critical components in safety-critical applications and compound AI systems. Despite their ubiquity, automated formal reasoning about their reliability has lagged significantly. Neural network verification is …
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Data-driven adaptive learning systems
"Adaptive learning systems are capable of providing more adaptive and efficient assessment and learning experiences for learners than traditional classroom settings. A conventional adaptive learning system involves a learner, a latent trait estimator, and a learning strategy/plan. The latent trait …
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Towards Intelligent Federated Learning Systems
… the privacy challenges of traditional machine learning have become more visible. Recent works leverage edge computing to preserve data privacy by keeping the data where it is (not shared during the training process), so-called "Edge Computing". In 2016, Google extended this idea to distributed …
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Empowered Learning Systems in Student Success
… <p>Although various support systems are attempted by public schools to cause changes in student motivation and academic performance, students continue to consistently under perform and doubt their academic potential. A literature review revealed a growing body of research …
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Machine learning systems in constrained environments
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Information quality assessment in e-learning systems.
E-learning systems provide a promising solution as an information exchanging channel. Improved technology could mean faster and easier access to information but does not necessarily ensure the quality of this information. Therefore it is essential to develop valid and reliable methods of quality …
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Information quality assessment in e-learning systems.
E-learning systems provide a promising solution as an information exchanging channel. Improved technology could mean faster and easier access to information but does not necessarily ensure the quality of this information. Therefore it is essential to develop valid and reliable methods of quality …
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Certifiably trustworthy deep learning systems at scale
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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On practical robustness of machine learning systems
… importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted perturbations to inputs which induce large change in output. We show that these adversarial …
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Towards Secure and Resilient Machine Learning Systems
Over the past decade, Machine Learning (ML) technologies have undergone revolutionary advancements, extending beyond traditional domains such as computer vision (CV) and natural language processing (NLP). One of the most significant breakthroughs is the development of transformer models, which …
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Towards Workload-aware Efficient Machine Learning Systems
Machine learning (ML) is transforming various aspects of our lives, driving the need for computing systems that efficiently support large-scale ML workloads. As models grow in size and complexity, existing systems struggle to adapt, limiting both performance and flexibility. Additionally, ML …
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Neuromorphic Learning Systems for Supervised and Unsupervised Applications
… the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two …
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Rich media content adaptation in e-learning systems
… difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning …
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Evolvable system architecture : design issues of learning systems
Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 2002.
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Building trustworthy machine learning systems in adversarial environments
Modern AI systems, particularly with the rise of big data and deep learning in the last decade, have greatly improved our daily life and at the same time created a long list of controversies. AI systems are often subject to malicious and stealthy subversion that jeopardizes their efficacy. Many of …
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Characterising Algorithm Debt in Machine and Deep Learning Systems
The integration of Machine and Deep Learning (ML/DL) into modern software systems has transformed application domains such as finance, healthcare, and autonomous technologies. However, the complexity of ML/DL algorithms, the unique development pipeline, and the stochastic training process introduce …
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Characterising Algorithm Debt in Machine and Deep Learning Systems
The integration of Machine and Deep Learning (ML/DL) into modern software systems has transformed application domains such as finance, healthcare, and autonomous technologies. However, the complexity of ML/DL algorithms, the unique development pipeline, and the stochastic training process introduce …
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Interaction Between Modules in Learning Systems for Vision Applications
Traditionally, vision systems extract features in a feedforward manner on the hierarchy; that is, certain modules extract low-level features and other modules make use of these low-level features to extract high-level features. Along with others in the research community we have worked on this …
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Understanding and mitigating privacy risk in machine learning systems
… have witnessed a rapid development in machine learning systems and a widespread increase of machine learning applications. However, with the widespread adoption of machine learning, privacy issues have emerged. This thesis studies the privacy risk in modern machine learning systems in two ways. …
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Machine Learning Systems for Unsupervised Time Series Anomaly Detection
… contributions in the form of both algorithms and systems. First, it introduces three models that enlarge the design space of unsupervised time series anomaly detection: TadGAN, which leverages adversarial reconstruction; AER, which unifies predictive and reconstructive objectives in a single …
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