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 11 of 11 for “"Structural Priors"”.
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Structural Priors in Deep Neural Networks
… generalization --- 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 …
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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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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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Modeling and editing 4D scenes by leveraging structural priors
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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A Bayesian model for dynamic functional connectivity estimation in the human brain with structural priors
… according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal …
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Efficient and Generalizable Machine Learning Models for Predicting Complex Dynamics
… from observed data. However, without explicit structural priors (built-in assumptions about the underlying dynamics) or additional contextual inputs, even modern high-capacity models that demonstrate impressive generalization typically require large and diverse training datasets, and may still …
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Minimal I-MAP MCMC for scalable structure discovery in causal DAG models
… methods but prevent the use of many natural structural priors and still have running time exponential in the maximum indegree of the true directed acyclic graph (DAG) of the BN. We here propose an alternative posterior approximation based on the observation that, if we incorporate empirical …
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Generative representations of 2D and 3D visual content: semantics, geometry, and appearance
… with human intent, incorporating geometric and structural priors, and enforcing physical plausibility in generated outputs. This thesis investigates generative representations of 2D and 3D visual content, with a focus on semantics, geometry, and appearance — three interconnected aspects that …
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Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans
… adult-pretrained models learn domain-agnostic structural priors, allowing them to reconstruct neonatal brain anatomy despite significant shifts in contrast, size, and motion characteristics. Crucially, fine-tuning with as few as three neonatal scans was shown to substantially improve the …
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Learning Structured World Models From and For Physical Interactions
… to introduce novel representations and integrate structural priors into the learning systems to model the dynamics at different levels of abstraction. I will discuss how we can make structural inferences about the underlying environment. I will also show how such structures can make model-based …
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Advanced Deep Learning Methods for the Automatic Analysis of Radar Sounder Data
… This method explicitly models the specific structural priors of RS data, such as the continuity of layers along the flight track and the ordered vertical sequence of subsurface materials. By encoding these properties using ad-hoc mechanisms directly into the network, the model achieves …