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Showing 1 to 4 of 4 for “"Machine Learning Robustness"”.
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Enhancing the robustness of machine learning models
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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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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A mixed-method approach to analyze the robustness of natural language processing classifiers
… a framework for the evaluation of NLP classifier robustness. Black-box attack algorithms are paired with a threat modelling system to apply a customizable set of constraints to the adversarial generation process. I introduce a mixed-method experimental design approach that combines metrics that …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
<p>Deep learning models, which form the backbone of modern ML systems, generalize poorly to small changes to the data distribution. They are also bad at signalling failure, making predictions with high confidence when their training data or fragile assumptions make them unlikely to make reasonable …