Zobrazeno 1 - 10 of 50 pro vyhledávání: '"Miguel Atencia"'
1
Akademický článek
Numerical Methods That Preserve a Lyapunov Function for Ordinary Differential Equations
Autor: Yadira Hernández-Solano, Miguel Atencia
Publikováno v: Mathematics, Vol 11, Iss 1, p 71 (2022)
The paper studies numerical methods that preserve a Lyapunov function of a dynamical system, i.e., numerical approximations whose energy decreases, just like in the original differential equation. With this aim, a discrete gradient method is implemen
Externí odkaz: https://doaj.org/article/c683eafcfcb545f1958df34cf4d761d1
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2
Akademický článek
A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.
Autor: Ruxandra Stoean, Catalin Stoean, Roberto Becerra-García, Rodolfo García-Bermúdez, Miguel Atencia, Francisco García-Lagos, Luis Velázquez-Pérez, Gonzalo Joya
Publikováno v: 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
Externí odkaz: https://doaj.org/article/99446791644046ccb6c77b3e6bb4a5d0
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3
Akademický článek
Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks
Autor: Miguel Atencia, Ruxandra Stoean, Gonzalo Joya
Publikováno v: Mathematics, Vol 8, Iss 8, p 1374 (2020)
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
Externí odkaz: https://doaj.org/article/d7eaceb62c9047b2800d2215160ff560
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4
Akademický článek
Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data
Autor: Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya
Publikováno v: Mathematics, Vol 8, Iss 7, 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,
Externí odkaz: https://doaj.org/article/95cbe64b03e64dabbba9efa7b36d9e65
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5
Akademický článek
Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
Autor: Catalin Stoean, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya, Gonzalo Joya
Publikováno v: Sensors, Vol 20, Iss 11, p 3032 (2020)
Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should
Externí odkaz: https://doaj.org/article/7640803b83bf419eb16a27c703639419
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8
On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs
Autor: Catalin Stoean, Nebojsa Bacanin, Ruxandra Stoean, Leonard Ionescu, Cristian Alecsa, Mircea Hotoleanu, Miguel Atencia, Gonzalo Joya
Publikováno v: RIUMA. Repositorio Institucional de la Universidad de Málaga
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It is impressive when one gets to see a hundreds or thousands years old artefact exhibited in the museum, whose appearance seems to have been untouched by centuries. Its restoration had been in the hands of a multidisciplinary team of experts and it
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42540fe963eaad17f852c7eff2e54a03
https://hdl.handle.net/10630/25290
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9
Numerical Methods That Preserve a Lyapunov Function for Ordinary Differential Equations
Autor: Miguel Atencia, Yadira Hernandez-Solano
Publikováno v: Mathematics; Volume 11; Issue 1; Pages: 71
The paper studies numerical methods that preserve a Lyapunov function of a dynamical system, i.e., numerical approximations whose energy decreases, just like in the original differential equation. With this aim, a discrete gradient method is implemen
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1f5b7d9f887fd37cb1262c64a1459e1
https://doi.org/10.3390/math11010071
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10
A Convolutional Neural Network as a Proxy for the XRF Approximation of the Chemical Composition of Archaeological Artefacts in the Presence of Inter-microscope Variability
Autor: Leonard Ionescu, Miguel Atencia, Ruxandra Stoean, Marinela Boicea, Gonzalo Joya, Catalin Stoean
Publikováno v: Advances in Computational Intelligence ISBN: 9783030850982
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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
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