45 citations · 147 across the 11 of their papers we have counts for
4 papers · 1 filter
Efficient n-body simulations using physics informed graph neural networks
Víctor Ramos-Osuna, Alberto Díaz-Álvarez, Raúl Lara-Cabrera
This paper presents a novel approach for accelerating n-body simulations by integrating a physics-informed graph neural networks (GNN) with traditional numerical methods. Our metho…
An evaluation framework for dimensionality reduction through sectional curvature
Raúl Lara-Cabrera, Ángel González-Prieto, Diego Pérez-López +2
Unsupervised machine learning lacks ground truth by definition. This poses a major difficulty when designing metrics to evaluate the performance of such algorithms. In sharp contra…
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto +1
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficul…
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
Fernando Ortega, Raúl Lara-Cabrera, Ángel González-Prieto +1
Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative…