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 25 for “"robust machine learning"”.
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Robust Machine Learning Methods in Solving Inverse Problems
… or ill-posed settings. Purely data-driven machine learning (ML) approaches have shown promising results by learning a direct mapping from measurements to ground-truth signals. While these methods often achieve superior reconstruction accuracy and faster runtime, they tend to lack …
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Principled approaches to robust machine learning and beyond
As we apply machine learning to more and more important tasks, it becomes increasingly important that these algorithms are robust to systematic, or worse, malicious, noise. Despite considerable interest, no efficient algorithms were known to be robust to such noise in high dimensional settings for …
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Robust machine learning models for high dimensional data interpretation
L'abstract è presente nell'allegato / the abstract is in the attachment
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Hierarchical, informed and robust machine learning for surgical tool management
… on the development of a computer vision and deep learning based system for the intelligent management of surgical tools. The work accomplished included the development of a new dataset, creation of state of the art techniques to cope with volume, variety and vision problems, and designing or …
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A robust machine learning approach for the prediction of allosteric binding sites
… a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This …
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PreCog : a robust machine learning system to predict failure in a virtualized environment
… aims to aid administrators by providing a robust future failure warning system statistically induced from past system behavior. In this work, we show that with the use of machine learning techniques such as Adaptive Boosting and Correlation-based Feature Selection, PreCog, without any prior …
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Robust Machine Learning Against Faults in Micro-Controllers and Stragglers in Distributed Training on the Cloud
Machine learning has become a critical part of many industries in the past decade. Optimally deploying ML models onto smaller devices and efficiently training more powerful ML models in parallel in different distributed system topologies have drawn interests. This thesis studies the robustness of …
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Motion-robust Machine Learning Methods for Region-of-Interest Tracking and Selective Magnetic Resonance Imaging with External Shim Arrays
… emphasize the vital necessity of implementing robust motion correction techniques in fetal MRI. This thesis presents a novel pipeline aimed at improving the robustness of fetal MRI against fetal motion. Central to this pipeline is the objective of achieving spatially selective Magnetic …
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A Practical Approach to Federated Learning
Machine learning models benefit from large and diverse training datasets. However, it is difficult for an individual organization to collect sufficiently diverse data. Additionally, the sensitivity of the data and government regulations such as GDPR, HIPPA, and CCPA restrict how organizations can …
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TOWARDS EFFICIENT LARGE-SCALE BI-LEVEL OPTIMIZATION, ALGORITHMS AND APPLICATIONS
… within the other. Recently, with the rise of machine learning, bi-level optimization has regained attention as a theoretical framework covering a wide range of machine learning problems, including hyperparameter optimization, neural architecture search, robust machine learning, meta-learning, …
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Decentralized AI for Methylation Data with Applications to Precision Health
… limiting collaboration and the development of robust machine learning models. This thesis proposes a decentralized artificial intelligence framework for analyzing DNA methylation data, enabling institutions to collaboratively train models without exchanging sensitive information. By taking …
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Predicting Material Properties with Machine Learned Interatomic Potentials
Machine learning interatomic potentials (ML-IPs) have emerged as a promising approach for bridging the gap between quantum electronic structure calculations (QM) and large scale classical molecular modeling simulations and have shifted the development of these many-body force fields to become …
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Empowering vision machine perception for robust telehealth applications
… computer vision for telehealth and deploying robust machine learning (ML) models for telehealth applications. Under the first focus, the Digitized Neurological Examination (DNE) system is introduced for comprehensive vision-based neurological examination using smartphones, validated for …
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Development of Machine Learning Models to Detect Dynamic Disturbances in Human Gait
Machine learning has transformed the medical field by automating tasks and achieving objectives that are closer to human cognitive capabilities. Gait is a series of intricate interactions for humans, and identifying impaired gait is critical for effective decision-making in clinical practice. …
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Machine Learning Methods for Churn Prediction and Infrastructure Resilience
This thesis investigates how advanced machine learning methods can effectively address two critical business challenges facing the telecommunications industry: short-term customer churn prediction and long-term infrastructure resilience to climate-driven disruptions. In the first part of this work, …
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Improved Vehicle Dynamics Sensing during Cornering for Trajectory Tracking using Robust Control and Intelligent Tires
… Different control algorithms, both classical and machine learning-based, have been developed for optimizing this vertical dynamics model. Experimental data has been collected by instrumenting a vehicle with in-tire accelerometers, IMU, GPS, and encoders for slalom and lane change maneuvers. …
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Understanding the Milky Way with Stars
… At the same time, cosmological simulations and machine learning techniques offer a bridge between the theory and observations. In this thesis, I combine observation of stellar kinematics and chemistry with cosmological simulations to understand the formation and evolution of the Milky Way and …
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Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images
… of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust …
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Robust and Fair Machine Learning under Distribution Shift
<p>Machine learning algorithms have been widely used in real world applications. The development of these techniques has brought huge benefits for many AI-related tasks, such as natural language processing, image classification, video analysis, and so forth. In traditional machine learning …
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Robust and Efficient AI-models for Medical Image Reconstruction, Segmentation, and Multimodal Knowledge Distillation
… constrained environments, and remain robust against noise and adversarial perturbations. Overcoming these barriers requires moving beyond narrowly data-driven systems toward AI frameworks that are both technically sophisticated and broadly adaptable to the complexity of clinical …
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