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 517 for “"Sparsity"”.
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Matrix Factorization: Nonnegativity, Sparsity and Independence
… which impose the requirements of nonnegativity, sparsity or independence.
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Deconvolution and sparsity based image restoration
… approach, dictionary learning methods with sparsity constraint on original image are designed to address the image denoising and in-painting problem. Recently, the sparse representation emerged as a useful regularization in ill-posed linear inverse problems. The main assumption in this …
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Deconvolution and sparsity based image restoration
… approach, dictionary learning methods with sparsity constraint on original image are designed to address the image denoising and in-painting problem. Recently, the sparse representation emerged as a useful regularization in ill-posed linear inverse problems. The main assumption in this …
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Strategies for Sparsity-based Time-Frequency Analyses
… In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments. In practice, missing data samples frequently occur during signal reception due to various reasons, e.g., propagation fading, measurement obstruction, removal of impulsive …
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Structured Sparsity Promoting Functions: Theory and Applications
… simple scheme to construct structured semiconvex sparsity promoting functions from convex sparsity promoting functions and their Moreau envelopes. Properties of these functions are developed by leveraging their structure. In particular, we show that the behavior of the constructed function can be …
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Enhancing robustness and sparsity via mathematical optimization
Esta tesis se centra en derivar métodos robustos o dispersos bajo la perspectiva de la optimización para problemas que tradicionalmente se engloban en los campos de la Estadística o de la Investigación Operativa. Concretamente, el objetivo de esta tesis doctoral es fusionar técnicas de optimización …
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Scalable sparsity structure learning using Bayesian methods
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity structure in various models. Estimation consistency and selection consistency …
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Sparsity in Machine Learning: Theory and Applications
Sparsity plays a key role in machine learning for several reasons including interpretability. Interpretability is sought either by practitioners or by scientists. Indeed, on one hand interpretability can be key in a practice such as in healthcare, in which black box models cannot be used for the …
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Sparsity and robustness in modern statistical estimation
… order to make reliable decisions. While sparsity and robustness are often closely related, the exact relationship and subsequent trade-offs are not always transparent. For example, convex penalties like the Lasso are often motivated by sparsity considerations, yet the success of these …
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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability
… focus of this thesis is on considering various sparsity and structural constraints within these methods and develop optimization based approaches to enhance training efficiency, inference, and/or interpretability. In the first part, we consider additive models with interactions under component …
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Efficient LLM training and inference with contextual sparsity
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Bayesian sparsity learning with variational automatic relevance determination
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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Sparsity-aware personalized recommender system via meta-learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Exploiting sparsity for machine learning in big data
… volume and complexity of such big data, however, sparsity has been recently studied to tackle this challenge. With reasonable assumptions and effective utilization of sparsity, we can learn models that are simpler, more efficient and robust to noise. The goal of this dissertation is studying and …
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Seismic ground-roll separation using sparsity promoting L1 minimization
The removal of coherent noise generated by surface waves in land based seismic is a prerequisite to imaging the subsurface. These surface waves, termed as ground roll, overlay important reflector information in both the t-x and f-k domains. Standard techniques of ground roll removal commonly alter …
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Resource Management for Distributed Estimation via Sparsity-Promoting Regularization
… gain matrix. Based on this association, a sparsity-promoting optimization framework is established, where the desire to reduce the number of selected sensors is characterized by a sparsity-promoting penalty term in the objective function. Instead of placing a hard constraint on sensor …
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Efficient Structure and Motion: Path Planning, Uncertainty and Sparsity
This thesis explores methods for solving the structure-and-motion problem in computer vision, the recovery of three-dimensional data from a series of two-dimensional image projections. The first paper investigates an alternative state space parametrization for use with the Kalman filter approach to …
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Learning to share: Bayesian approaches to sparsity and transfer
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Dynamic sparsity: enabling efficient, interpretable and generalizable sequence models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications
… the demand-supply gap through the lens of sparsity. In Part I, we first develop algorithms, SparseViT and SparseRefine, which identify sparsity within dense input data. We then introduce new sparse primitives, PVCNN and FlatFormer, to efficiently process inputs with sparsity. In Part II, we …
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