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 355 for “"priors"”.
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Convex modeling with priors
… standard methods in statistical regression with priors. These priors might include how the output series should behave or the specifics of the functional form relating inputs to outputs. My approach is optimization driven: by formulating a concise set of goals and constraints, approximate models …
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On Independent Reference Priors
… of prior has been of great interest. Subjective priors are ideal if sufficient information on priors is available. However, in practice, we cannot collect enough information on priors. Then objective priors are a good substitute for subjective priors. In this dissertation, an independent …
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Bayesian dynamic borrowing strategies with power priors and quantifying prior information for circular priors.
… strategies for the assessment of circular priors. Chapter two introduces a novel dynamic borrowing method that can be applied in both clinical and non-clinical settings. Recent approaches such as that in Thompson et al. (2021) do not accommodate situations with limited current sample sizes. …
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Shape Reconstruction with Topological Priors
<p>We show topological priors play an important role in solving the inverse problem of shape reconstruction. We classify the applications into 1D, 2D, and 3D cases:</p><p>In 1D, we show that the persistent extrema of the curvature function of a closed curve are useful for shape simplication. In 2D, …
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Robot learning with strong priors
… systems and perform robot learning with strong priors. In this thesis, we exemplify the value of imposing strong priors in robot learning (or machine learning in general) via both practical experiments and theories with mild assumptions. Empirically, by proposing new algorithms and systems, we …
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Biophysical dynamical priors in machine learning
… box dynamical biases to explicit biophysical priors encoded directly in the model. The resulting techniques are studied under the lens of the latent force paradigm, a combination of nonparametric mechanistic and data-driven approaches, leading to improved biological interpretability and a …
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Structural Priors in Deep Neural Networks
… --- what we propose to denote as structural priors. We present two such novel structural priors for convolutional neural networks, and evaluate them in state-of-the-art image classification CNN architectures. The first of these methods proposes to exploit our knowledge of the low-rank nature …
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Active learning using meta-learned priors
Deep learning models have found enormous success across a variety of displines, but training the parameters within these models generally requires huge amounts of labelled data. One technique to reduce the burden of this data requirement is meta-learning, which involves extracting knowledge from …
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Learning 3D Robotics Perception using Inductive Priors
… of learning with structured inductive bias and priors to design approaches and algorithms unlocking the potential of principle-centric intelligence for the real-world. Prior knowledge (priors for short), often available in terms of past experience as well as assumptions of how the world works, …
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Development of Informative Priors in Microarray Studies
… current Bayesian analyses use empirical or flat priors. We present a Perl script to build an informative prior by mining online databases for similar microarray experiments. Four prior distributions are investigated: a power prior including information from multiple previous experiments, an …
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Non-Gaussian Stochastic Process Priors for Learning
Non-Gaussian statistics naturally emerge as a fundamental concept in the study of real-world phenomena where standard Gaussian models often fall short in capturing the true variability and extreme behaviour. These characteristics are especially prevalent in fields such as finance, climate science, …
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Seeing Beyond Limits with Physics-Informed Priors
… deep denoisers. By embedding physics-informed priors into this optimization, it aims to surpass conventional limits in dimensionality and visibility. First, I develop Privacy Dual Imaging using an ambient light sensor. This approach tackles both dimensionality and visibility challenges when …
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Structural Priors for Active Learning on Robots
A primary hindrance to neural networks in robotic applications is data efficiency; collecting data on a real robot is slow and expensive. Active learning, in which the learner chooses the data that will best accelerate learning, has been shown to reduce data requirements in machine learning and …
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Using Dirichlet Process Priors For Bayesian Mixture Clustering
We describe a non-parametric Bayesian model using genotype data to classify individuals among populations where the total number of populations is unknown. The model assumes that a population is characterized by a set of allele frequencies that follow multinomial distributions. The Dirichlet …
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Harnessing data priors to mitigate 3D data scarcity
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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Learning compact neural network representations with structural priors
DSpace SAF Submission Ingestion Package generated from Vireo submission #13747 on 2019-08-22 at 15:07:23
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Bayesian matrix factorisation: inference, priors, and data integration
… matrix factorisation models and Bayesian priors. Through these models and experiments we support our hypothesis and provide novel insights into the best ways to use Bayesian matrix factorisation methods for predictive purposes.
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Genetic, clinical and population priors for brain images
We develop mathematical models that exploit external information to improve analysis of a medical scan. Medical images enable visualization of the human body, and are central in clinical practice and many large-scale scientific studies. Medical image analysis uses computational models to interpret …
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Controlling Image Synthesis with Emergent and Designed Priors
… generative models using emergent and designed priors. Emergent priors leverage the capabilities of a pre-trained generator to infer how the world operates, simply by training on large quantities of data. On the other hand, designed priors use built-in constraints to enforce desired properties …
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Global and local motion priors and their applications
With the rising popularity and accessibility of cameras as well as the arrival of popular video sharing websites like YouTube.com, Google Video, veoh.com, and many others, large quantities of video are produced and available everyday. With all this data, it becomes necessary to find ways of …
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