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 5 of 5 for “"explainable models"”.
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Deep heterogeneous superpixel neural networks for image analysis and feature extraction
… Specifically, we have created superpixel models that join graphical neural network techniques and multiple-instance learning to achieve weakly supervised object detection and generate precise object bounding without pixel-level training labels. This dissection and the subsequent learning …
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Applying Language Models To Patient Health Records: Acronym Expansion, Long Document Classification and Explainable Predictions
… medical acronyms in context, (2) building models that can analyze multi-modal data (structured and unstructured patient EHR data) that includes lengthy clinical notes to study a stigmatized condition, namely opioid prescribing patterns and opioid use disorder (OUD) risk, and (3) developing …
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NETWORKS OF GROUP EQUIVARIANT NON-EXPANSIVE OPERATORS FOR ARTIFICIAL INTELLIGENCE. MODELS, APPLICATIONS AND INTERPRETABILITY.
… can be deceptive or counterfeit. The pursuit of eXplainable Artificial Intelligence (XAI) aims to develop methods that clarify the decision-making processes of black-box AI systems, making them more understandable and trustworthy for end users, in line with regulatory and policy demands. Another …
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An Explainable Artificial Intelligence Approach Based on Deep Type-2 Fuzzy Logic System
… explainability reduces the effectiveness of AI models in regulated applications (such as medical, financial, etc.), where it is essential to explain the model operation and how it arrived at a given prediction. The need for explainability in AI has led to a new line of research that focuses on …
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Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment
… Structure-Activity Relationship (QSAR) models – a key part of the Next Generation Risk Assessment strategy for animal-free safety. Machine learning methods are often employed to build QSAR models, but these “black box” functions still need to be validated robustly before being included …