Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Gonzalo Joya"'
Publikováno v:
Symmetry
Volume 13
Issue 8
Symmetry, Vol 13, Iss 1534, p 1534 (2021)
Volume 13
Issue 8
Symmetry, Vol 13, Iss 1534, p 1534 (2021)
A communication network management system takes the measurements of its state variables at specific instants of time, considering them constant in the interval between two consecutive measurements. Nevertheless, this assumption is not true, since the
Publikováno v:
Advances in Computational Intelligence ISBN: 9783030850296
Advances in Computational Intelligence ISBN: 9783030850982
Advances in Computational Intelligence ISBN: 9783030850982
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b81f9e12e65991d30e0a7eeb3ecb0b3
https://doi.org/10.1007/978-3-030-85030-2_51
https://doi.org/10.1007/978-3-030-85030-2_51
Autor:
Leonard Ionescu, Miguel Atencia, Ruxandra Stoean, Marinela Boicea, Gonzalo Joya, Catalin Stoean
Publikováno v:
Advances in Computational Intelligence ISBN: 9783030850982
IWANN (2)
IWANN (2)
The paper puts forward a convolutional neural network model for multi-output regression, which is trained on images from two distinct microscope types to estimate the concentration of a pair of chemical elements from the surface of archaeological met
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e75609b4bd00c77d3f01eda0adcd235
https://doi.org/10.1007/978-3-030-85099-9_21
https://doi.org/10.1007/978-3-030-85099-9_21
Autor:
Ruxandra Stoean, C. Patru, Andreea Sorina Marcu, Dan Ruican, Dominic Gabriel Iliescu, Vlad Ilie, Catalin Stoean, Miguel Atencia, Mircea Hotoleanu, Gonzalo Joya, Rares Trocan, Rodica Daniela Nagy
Publikováno v:
Advances in Computational Intelligence ISBN: 9783030850296
IWANN (1)
IWANN (1)
The current paper challenges convolutional neural networks to address the computationally undebated task of recognizing four key views in first trimester fetal heart scanning (the aorta, the arches, the atrioventricular flows and the crossing of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96284183159797fb1026ab149778d2e7
https://doi.org/10.1007/978-3-030-85030-2_1
https://doi.org/10.1007/978-3-030-85030-2_1
Publikováno v:
Mathematics, Vol 8, Iss 1374, p 1374 (2020)
Mathematics
Volume 8
Issue 8
Mathematics
Volume 8
Issue 8
The application of echo state networks to time series prediction has provided notable results, favored by their reduced computational cost, since the connection weights require no learning. However, there is a need for general methods that guide the
Publikováno v:
Mathematics
Volume 8
Issue 7
Mathematics, Vol 8, Iss 1078, p 1078 (2020)
Volume 8
Issue 7
Mathematics, Vol 8, Iss 1078, p 1078 (2020)
Uncertainty quantification in deep learning models is especially important for the medical applications of this complex and successful type of neural architectures. One popular technique is Monte Carlo dropout that gives a sample output for a record,
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616151
ICANN (2)
RIUMA: Repositorio Institucional de la Universidad de Málaga
Universidad de Málaga
RIUMA. Repositorio Institucional de la Universidad de Málaga
instname
ICANN (2)
RIUMA: Repositorio Institucional de la Universidad de Málaga
Universidad de Málaga
RIUMA. Repositorio Institucional de la Universidad de Málaga
instname
This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85239cf09cd5c6e2c0e9e74bf6c3da48
https://doi.org/10.1007/978-3-030-61616-8_39
https://doi.org/10.1007/978-3-030-61616-8_39
Autor:
Francisco García-Lagos, Ruxandra Stoean, Luis Velázquez-Pérez, Roberto Becerra-García, Gonzalo Joya, Miguel Atencia, Rodolfo García-Bermúdez, Catalin Stoean
Publikováno v:
PLoS ONE
PLoS ONE, Vol 15, Iss 7, p e0236401 (2020)
PLoS ONE, Vol 15, Iss 7, p e0236401 (2020)
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories
Publikováno v:
Neural Computing and Applications. 32:309-311