Zobrazeno 1 - 10
of 185
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, the called eXplainable artificial intelligence (XAI). Existing efforts primarily focus on generating and evaluating explanations for black-box mod
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