Výsledky vyhledávání - "Moscholios, Ioannis D."

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Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting

Autor: Tsoumplekas, Georgios, Siniosoglou, Ilias, Argyriou, Vasileios, Moscholios, Ioannis D., Sarigiannidis, Panagiotis

The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple provide

Externí odkaz: http://arxiv.org/abs/2405.20430

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Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

Autor: Asimopoulos, Dimitris, Siniosoglou, Ilias, Argyriou, Vasileios, Karamitsou, Thomai, Fountoukidis, Eleftherios, Goudos, Sotirios K., Moscholios, Ioannis D., Psannis, Konstantinos E., Sarigiannidis, Panagiotis

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text a

Externí odkaz: http://arxiv.org/abs/2404.14465

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Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings

Autor: Tsoumplekas, Georgios, Li, Vladislav, Siniosoglou, Ilias, Argyriou, Vasileios, Goudos, Sotirios K., Moscholios, Ioannis D., Radoglou-Grammatikis, Panagiotis, Sarigiannidis, Panagiotis

In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critic

Externí odkaz: http://arxiv.org/abs/2403.06631

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Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches

Autor: Tsoumplekas, Georgios, Li, Vladislav, Argyriou, Vasileios, Lytos, Anastasios, Fountoukidis, Eleftherios, Goudos, Sotirios K., Moscholios, Ioannis D., Sarigiannidis, Panagiotis

Despite deep learning's widespread success, its data-hungry and computationally expensive nature makes it impractical for many data-constrained real-world applications. Few-Shot Learning (FSL) aims to address these limitations by enabling rapid adapt

Externí odkaz: http://arxiv.org/abs/2402.03017

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