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 135 for “"Explainability"”.
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Dissecting Deep Language Models: The Explainability and Bias Perspective
L'abstract è presente nell'allegato / the abstract is in the attachment
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Pairwise Matching of Intermediate Representations for Fine-grained Explainability
… often subtle and highly localized, and existing explainability techniques for deep learning models are often too diffuse to provide useful and interpretable explanations. We propose a new explainability method (PAIR-X) that leverages both intermediate model activations and backpropagated …
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Scalable black-box model explainability through low-dimensional visualizations
Two methods are proposed to provide visual intuitive explanations for how black-box models work. The first is a projection pursuit-based method that seeks to provide data-point specific explanations. The second is a generalized additive model approach that seeks to explain the model on a more …
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Appley: Approximate Shapley Values for Model Explainability in Linear Time
… discovering new knowledge about a problem. Model explainability has been an active area of research for some time now, but the problem is still far from being solved. An established way of model explanation (also known as variable attribution) is to assign a score to each variable, which …
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Advancing Deep Learning Across Modalities: From Explainability to Multimodal Content Understanding
L'abstract è presente nell'allegato / the abstract is in the attachment
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Understanding Neural Burst Patterns Through Graph Neural Network Explainability in Simulated Neuronal Networks
… patterns driving model predictions. This explainability analysis revealed which specific neurons and synaptic connections the model deemed most critical for each prediction. This work demonstrates how explainable AI can transform our understanding of complex neural dynamics, providing …
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Bayesian autoencoders for anomaly detection: Design, uncertainty quantification, and explainability with industrial applications
… design, (2) uncertainty quantification, and (3) explainability. The BAEs ground design and analysis on a well-studied probabilistic foundation and implement Bayesian model averaging to improve detection performance. This thesis compares various design choices of BAEs. The use of Bernoulli …
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Exploring Machine Learning, Feature Engineering, and Explainability to Constrain Spica’s Apsidal Constant through MESA Simulations
… apsidal constant through feature engineering and explainability methods. Lastly, the research seeks to develop a weighted fusion approach, leveraging an ensemble voting regressor capable of predicting the apsidal constant with unseen data, ultimately enhancing prediction accuracy, robustness, and …
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Advancing Explainability in Multi-Label Classification for Tomato Disease Detection Using Machine Learning Interpretability Techniques
<p>Plant diseases pose a significant threat to global food security, affecting crop yield, quality, and overall agricultural productivity. Traditionally, diagnosing plant diseases has relied on timeconsuming visual inspections by experts, which can often lead to errors. With the rapid growth of …
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Towards Explainability and Domain Knowledge-inspired Design of Online Real-Time Learning Techniques in NextG Wireless Systems
… up with first principles, resulting in enhanced explainability and interpretability of RC-based architectures, thereby turning opaque ``black-box'' models into intuitive ``gray-box'' models. This solid foundation, founded on signal processing and information theory fundamentals, enables the …
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Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics.
Evolutionary algorithms (EAs) are the principal focus of research study in Evolutionary Computing (EC). In EC, naturally occurring processes designed to drive success in nature are simulated for a similar purpose in numerical optimisation. Such processes include natural selection, genetic mutation …
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A User-Centered Design Approach to Evaluating the Usability of Automated Essay Scoring Systems
… limited research has specifically focused on AI explainability and algorithm transparency and their influence on the usability of these platforms. To address this gap, we conducted a qualitative study on an AI-based essay writing and grading platform, with a primary focus to explore the …
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Self-Training for Natural Language Processing
… inference skills, unseen text styles, and explainability. In this thesis, we explore self-training methods for mitigating the data distribution gaps between training and evaluation domains and tasks. In contrast to traditional self-training methods that study the best practice of training …
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Trustworthy Machine Learning: From Algorithmic Transparency to Decision Support
… Algorithmic transparency tools, such as explainability and uncertainty estimates, demonstrate the trustworthiness of a model to a decision-maker. In this thesis, we first explore how practitioners use explainability in industry. Through an interview study, we find that, while engineers …
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