Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Prieto, Angel"'
Context: This work is part of a research project whose ultimate goal is to systematize theory building in qualitative research in the field of software engineering. The proposed methodology involves four phases: conceptualization, operationalization,
Externí odkaz:
http://arxiv.org/abs/2412.02384
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 7, number 4, Pages 18-26, 2022
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This p
Externí odkaz:
http://arxiv.org/abs/2410.16838
Given $G$ an algebraic reductive group over an algebraically closed field of characteristic zero and $\Gamma$ a finitely generated group, we provide a stratification of the $G$-character variety of $\Gamma$ in terms of conjugacy classes of parabolic
Externí odkaz:
http://arxiv.org/abs/2408.03111
In this paper, we study the geometry of the moduli space of representations of the fundamental group of the complement of a torus link into an algebraic group G, an algebraic variety known as the G-character variety of the torus link. These torus lin
Externí odkaz:
http://arxiv.org/abs/2402.12286
In this paper, we introduce Topological Quantum Field Theories (TQFTs) generalizing the arithmetic computations done by Hausel and Rodr\'iguez-Villegas and the geometric construction done by Logares, Mu\~noz, and Newstead to study cohomological invar
Externí odkaz:
http://arxiv.org/abs/2309.15331
Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This causes a detriment to the predicti
Externí odkaz:
http://arxiv.org/abs/2308.02058
Autor:
Lara-Cabrera, Raúl, González-Prieto, Ángel, Pérez-López, Diego, Trujillo, Diego, Ortega, Fernando
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 contrast with supervised learning, for which plenty of quality metrics have
Externí odkaz:
http://arxiv.org/abs/2303.09909
We describe the geometry of the character variety of representations of the fundamental group of the complement of a Hopf link with $n$ twists, namely $\Gamma_{n}=\langle x,y \,| \, [x^n,y]=1 \rangle$ into the group $\mathrm{SU}(r)$. For arbitrary ra
Externí odkaz:
http://arxiv.org/abs/2303.06218
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also reliability,
Externí odkaz:
http://arxiv.org/abs/2210.10619
Generative Adversarial Networks (GANs) are powerful Machine Learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable and typi
Externí odkaz:
http://arxiv.org/abs/2209.01842