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Advances in Probabilistic Generative Models: Normalizing Flows, Multi-View Learning, and Linear Dynamical Systems
DownloadFall 2020
This thesis considers some aspects of generative models including my contributions in deep probabilistic generative architectures and linear dynamical systems. First, some advances in deep probabilistic generative models are contributed. Flow-based generative modelling is an emerging and highly...
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Advances in Quantitative Susceptibility Mapping for Human Brain: Applications in Hemorrhage, Motion, Blood Vessels
DownloadFall 2022
Quantitative Susceptibility Mapping (QSM) is an emerging postprocessing method, computed from phase images, which is finding wide application in quantifying iron content in healthy and pathological tissue. However, QSM is still not commonly used in clinical practice. This thesis discusses the...
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Spring 2023
The fluid mechanics literature suggests that the ability to manipulate the very-large-scale motions (VLSMs) that exist within turbulent boundary layers (TBLs) would provide influence over the unwanted drag forces, noise, and vibrations associated with these flows. The ability to suppress these...
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Spring 2023
Reinforcement learning (RL) defines a general computational problem where the learner must learn to make good decisions through interactive experience. To be effective in solving this problem, the learner must be able to explore the environment, make accurate predictions about the future, and...
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Advances on Fabrication and Application of Through Silicon Via for Radio Frequency Circuits
DownloadSpring 2019
Through silicon via (TSV) has been considered as an astonishing milestone in the evolution of three-dimensional integrated circuit (3D IC), because of its exclusive and pivotal function of providing signal exchanging paths in the horizontal direction to stacked layers efficiently. Unfortunately,...