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Full-Text Articles in Engineering
Regularized Coordinate-Based Neural Representation Learning For Optical Tomography, Renhao Liu
Regularized Coordinate-Based Neural Representation Learning For Optical Tomography, Renhao Liu
McKelvey School of Engineering Theses & Dissertations
Neural representation learning recently shows outstanding performance in several computer vision tasks. In this thesis, we propose a novel self-supervised neural represented reconstruction method for optical tomography. Our method uses a Multi-Layer Perceptron (MLP) network to represent the target sample without the need for any ground truth or training data. The MLP weights serve as a latent representation of the target object. Any desired permittivity information can be inferred by querying the neural network within the sample domain. We also investigate applying regularization to implicitly restrict the manifold of MLP for better performance. Our experiments produce low artifacts results with …
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
McKelvey School of Engineering Theses & Dissertations
Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …
Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi
McKelvey School of Engineering Theses & Dissertations
A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …