septiembre 2021

Hernández, A., Amigó, J.M. (2021) «The Need for More Integration Between Machine Learning and Neuroscience», Nonlinear Dynamics, Chaos, and Complexity, Springer, 9-19

Adrián Hernández and José María Amigó (Universidad Miguel Hernández de Elche)
Abstract: Neuroscience and machine learning are two interrelated fields with obvious synergies. In recent years we have seen profound advances in these fields and in the integration between them. However, there remain challenges for a greater integration between the two disciplines. In this chapter we […]

By agustin.diazg, ago

Galarza, M., Etus, V., Sosa, F., Argañaraz, R., Mantese, B., Gazzeri, R., Montoya, C.G., de la Rosa, P., Guerrero, A.L., Chaban, G., Giménez, Á., Amigó, J.M. (2021) «Flow ventricular catheters for shunted hydrocephalus: initial clinical results», Child’s Nervous System, 37 (3):903–911

Marcelo Galarza, Pedro de la Rosa, Antonio López Guerrero (Hospital Virgen de la Arrixaca, Murcia), Volkan Etüş (Kocaeli Üniversitesi, Kocaeli, Turkey), Fidel P. Sosa (Hospital Aleman, Buenos Aires, Argentina), Romina Argañaraz, Beatriz Elida  Mantese (Department of Neurosurgery, Hospital Nacional de Pediatría J. Garrahan, Buenos Aires, Argentina), Roberto Gazzeri (Department of Neurosurgery, San Giovanni Addolorata Hospital, Rome, Italy), Crhistian Garcia […]

By agustin.diazg, ago

Alcaraz, J., Anton-Sanchez, L., Aparicio, J., Monge, J.F., Ramón, N. (2021) «Russell Graph efficiency measures in Data Envelopment Analysis: The multiplicative approach», European Journal of Operational Research, 28 (3) : 646-671

Javier Alcaraz,  Laura Antón,  Juan Aparicio, Juan Francisco Monge, and Nuria Ramón(Universidad Miguel Hernández de Elche)
Abstract: The measurement of technical efficiency is a topic of great interest. Since the beginning, many researchers have developed new approaches to gauge technical efficiency, mainly in the non-parametric area of Data Envelopment Analysis (DEA). However, the first measures in […]

By agustin.diazg, ago

Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems

Título: Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems
Ponente:  Antonio Linero (Universidad de Texas en Austin, EEUU)
Organizador: Xavier Barber
Fecha: Viernes 17 de septiembre de 2021 a las 17:00 horas.
Lugar: Online.

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Abstract: In this talk we will make the case for using Bayesian decision tree ensembles, such as Bayesian additive regression trees (BART), for addressing some fully-nonparametric problems. We present models […]

By agustin.diazg, ago
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