Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Pollastro, Andrea"'
The specialized language and complex concepts in physics pose significant challenges for information extraction through Natural Language Processing (NLP). Central to effective NLP applications is the text embedding model, which converts text into den
Externí odkaz:
http://arxiv.org/abs/2408.09574
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
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
Publikováno v:
IEEE Access, vol. 11, pp. 67098-67112, 2023
In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural conditio
Externí odkaz:
http://arxiv.org/abs/2210.05674
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
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
Publikováno v:
In Pattern Recognition December 2023 144
Publikováno v:
In Engineering Applications of Artificial Intelligence August 2023 123 Part A
Akademický článek
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