66 citations · 114 across the 10 of their papers we have counts for
13 papers
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Alberto D. Cencillo, Leonardo Concepción, Isaac Triguero +1
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the c…
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
Alberto D. Cencillo, Leonardo Concepción, Julián Luengo +1
Time series anomaly detection is central to monitoring industrial and scientific equipment, where anomalous sensor readings can precede failures. As such equipment usually carries…
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability
Iván Sevillano-García, Julián Luengo, Francisco Herrera
Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences. Howeve…
Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting
Ignacio Aguilera-Martos, Andrés Herrera-Poyatos, Julián Luengo +1
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,…
X-SHIELD: Regularization for eXplainable Artificial Intelligence
Iván Sevillano-García, Julián Luengo, Francisco Herrera
As artificial intelligence systems become integral across domains, the demand for explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts prima…
A Survey on Semi-Supervised Semantic Segmentation
Adrian Peláez-Vegas, Pablo Mesejo, Julián Luengo
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 cos…
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study
Iván Sevillano-García, Julián Luengo-Martín, Francisco Herrera
Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality…
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)
Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo +4
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 tempor…
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training
Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
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 le…
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms
Jacinto Carrasco, Irina Markova, David López +6
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 o…
Label Noise Filtering Techniques to Improve Monotonic Classification
José-Ramón Cano, Julián Luengo, Salvador García
The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the prob…
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
Anabel Gómez-Ríos, Siham Tabik, Julián Luengo +3
The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in…
Enabling Smart Data: Noise filtering in Big Data classification
Diego García-Gil, Julián Luengo, Salvador García +1
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale…