Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Isgrò Francesco"'
Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects appr
Externí odkaz:
http://arxiv.org/abs/2406.17537
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial Intelligence (XAI) inspects internal mechanisms of AI models providing
Externí odkaz:
http://arxiv.org/abs/2403.10373
Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, ut
Externí odkaz:
http://arxiv.org/abs/2401.13796
In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic i
Externí odkaz:
http://arxiv.org/abs/2306.06146
Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models
Externí odkaz:
http://arxiv.org/abs/2306.05801
Publikováno v:
3rd Italian Workshop on Explainable Artificial Intelligence, XAI.it 2022; Conference date: 28 November 2022 through 3 December 2022
An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance
Externí odkaz:
http://arxiv.org/abs/2210.06554
In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems t
Externí odkaz:
http://arxiv.org/abs/2210.01081
A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine Learning (ML) non-interpretable models after the training. Recently,
Externí odkaz:
http://arxiv.org/abs/2106.05037
Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest be
Externí odkaz:
http://arxiv.org/abs/2105.10377
Autor:
Briz Susana, Anzalone Anna, Isgrò Francesco, Cremonini Roberto, Tabone Ilaria, Bertainav Mario, Rodríguez Irene, Fernández-Gómez Isabel, Castro Antonio J. de
Publikováno v:
EPJ Web of Conferences, Vol 89, p 03004 (2015)
The main telescope of JEM-EUSO will determine Ultra High Energy Cosmic Ray properties by measuring the UV fluorescence light generated in the interaction between the cosmic rays and the atmosphere. Therefore, cloud information is crucial for a proper
Externí odkaz:
https://doaj.org/article/d9e0915f9494411cab98b9b64c05f736
