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Low-rank structures for robustness in Deep Learning

The notion of the general stability of deep neural networks has been widely discussed virtually since their introduction. The overall performance of DNN models is inherently linked to overparameterization; however, the excessive number of parameters introduces natural instabilities in the model’s output. As a result, one frequently considers the effects of the extreme perturbations of the input data, known as adversarial attacks, to characterize the robustness of the models. Over the years, ...

A systematic mapping study on automated negotiation for autonomous intelligent systems

Autonomous intelligent systems are known as artificial intelligence software entities that can act on their own and can take decisions without any human intervention. The communication between such systems to reach an agreement for problem-solving is known as automated negotiation. This study aims to systematically identify and analyze the literature on automated negotiation from four distinct viewpoints: (1) the existing literature on negotiation with focus on automation, (2) the specific pu...

Radon-Induced Lead Isotopes in XENONnT: Studies on Branching Ratios and Surface Background

The XENONnT experiment, a dual-phase liquid Xenon time projection chamber (TPC), hosted in the Laboratori Nazionali del Gran Sasso underground facilities, is designed to achieve unprecedented sensitivity in the direct detection of WIMP dark matter. As the sensitivity of liquid Xenon detectors improves, background contributions from intrinsic radioactive contaminants become increasingly significant. Among these, Radon-induced Lead isotopes Pb-212, Pb-214 and Pb-210 pose a major challenge, affe...

Essays on Resilience and Economic Performance

This dissertation examines the dynamics between resilience and economic development, with a particular focus on how resilience interacts with different dimensions of economic performance. While resilience has gained increasing attention in both academic and policy debates, its role within long-term economic development remains to some extent underexplored. This thesis contributes to bridging this gap by investigating resilience from multiple perspectives, emphasizing the localized and context...

Counterfactual Explainability in Graphs: Foundations, Generative Methods, and Ensemble Techniques

Graph Neural Networks (GNNs) have revolutionized numerous domains, from social network analysis and recommender systems to biochemistry, by leveraging the rich interplay between node attributes and their relationships. These models excel at capturing the complexities of graph-structured data, driving advancements in prediction accuracy. However, their black-box nature poses a critical challenge in high-stakes fields like healthcare and finance, where transparency, trust, and fairness are non-...

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Prodotti recenti

Low-rank structures for robustness in Deep Learning

A systematic mapping study on automated negotiation for autonomous intelligent systems

Radon-Induced Lead Isotopes in XENONnT: Studies on Branching Ratios and Surface Background

Essays on Resilience and Economic Performance

Counterfactual Explainability in Graphs: Foundations, Generative Methods, and Ensemble Techniques

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