Zobrazeno 1 - 10
of 183
pro vyhledávání: '"Luengo, Julián"'
Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis, especially for long-horizon forecasting, showcasing promising results
Externí odkaz:
http://arxiv.org/abs/2410.03805
As Artificial Intelligence systems become integral across domains, the demand for explainability grows. While the effort by the scientific community is focused on obtaining a better explanation for the model, it is important not to ignore the potenti
Externí odkaz:
http://arxiv.org/abs/2404.02611
Semantic segmentation is one of the most challenging tasks in computer vision. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. In this scenario, it makes sense to approac
Externí odkaz:
http://arxiv.org/abs/2302.09899
Autor:
Aguilera-Martos, Ignacio, García-Vico, Ángel M., Luengo, Julián, Damas, Sergio, Melero, Francisco J., Valle-Alonso, José Javier, Herrera, Francisco
The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such
Externí odkaz:
http://arxiv.org/abs/2206.03179
Autor:
García-Gil, Diego, López, David, Argüelles-Martino, Daniel, Carrasco, Jacinto, Aguilera-Martos, Ignacio, Luengo, Julián, Herrera, Francisco
Publikováno v:
In Information Sciences February 2025 690
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a result, it is widely used. However, like other machine learning algorithms, deep learning, and convolutional neural networks (CNNs) in particular, perfor
Externí odkaz:
http://arxiv.org/abs/2109.03748
Autor:
Carrasco, Jacinto, Markova, Irina, López, David, Aguilera, Ignacio, García, Diego, García-Barzana, Marta, Arias-Rodil, Manuel, Luengo, Julián, Herrera, Francisco
The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a s
Externí odkaz:
http://arxiv.org/abs/2105.12818
Autor:
López, David, Aguilera-Martos, Ignacio, García-Barzana, Marta, Herrera, Francisco, García-Gil, Diego, Luengo, Julián
Publikováno v:
In Information Fusion December 2023 100
Autor:
Aguilera-Martos, Ignacio, García-Barzana, Marta, García-Gil, Diego, Carrasco, Jacinto, López, David, Luengo, Julián, Herrera, Francisco
Publikováno v:
In Neurocomputing 1 August 2023 544