Zobrazeno 1 - 10
of 581
pro vyhledávání: '"Lobo, A. L."'
Publikováno v:
In: Quinti\'an, H., et al. Hybrid Artificial Intelligent Systems. HAIS 2024. Lecture Notes in Computer Science(), vol 14857
Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when
Externí odkaz:
http://arxiv.org/abs/2411.00876
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data are partially labeled. The extreme case where such a supervision is in
Externí odkaz:
http://arxiv.org/abs/2407.05379
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage. Open Set Recognition addresses this issue by devising models capable o
Externí odkaz:
http://arxiv.org/abs/2312.08785
Autor:
Osaba, Eneko, Benguria, Gorka, Lobo, Jesus L., Diaz-de-Arcaya, Josu, Alonso, Juncal, Etxaniz, Iñaki
In the last years, one of the fields of artificial intelligence that has been investigated the most is nature-inspired computing. The research done on this specific topic showcases the interest that sparks in researchers and practitioners, who put th
Externí odkaz:
http://arxiv.org/abs/2311.10767
AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion
Externí odkaz:
http://arxiv.org/abs/2303.07940
Autor:
Seras, Aitor Martinez, Del Ser, Javier, Lobo, Jesus L., Garcia-Bringas, Pablo, Kasabov, Nikola
Research around Spiking Neural Networks has ignited during the last years due to their advantages when compared to traditional neural networks, including their efficient processing and inherent ability to model complex temporal dynamics. Despite thes
Externí odkaz:
http://arxiv.org/abs/2210.00894
Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects
Publikováno v:
In Neurocomputing 28 September 2024 599
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutio
Externí odkaz:
http://arxiv.org/abs/2010.03917
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learnin
Externí odkaz:
http://arxiv.org/abs/2009.09677
Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synerg
Externí odkaz:
http://arxiv.org/abs/2005.05066