Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Fernando Perez-cruz"'
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-20 (2023)
Abstract The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicologi
Externí odkaz:
https://doaj.org/article/94727c1144ae4964a6c9a9436b41f898
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Type 2 diabetes mellitus (T2DM) is associated with the development of chronic comorbidities, which can lead to high drug utilization and adverse events. We aimed to identify common comorbidity clusters and explore the progression over time i
Externí odkaz:
https://doaj.org/article/f788b9f6e961429399c767e9d3dd4571
Autor:
Melanie F. Pradier, Bernhard Reis, Lori Jukofsky, Francesca Milletti, Toshihiko Ohtomo, Fernando Perez-Cruz, Oscar Puig
Publikováno v:
BMC Cancer, Vol 19, Iss 1, Pp 1-7 (2019)
Abstract Background Codrituzumab, a humanized monoclonal antibody against Glypican-3 (GPC3), which is expressed in hepatocellular carcinoma (HCC), was tested in a randomized phase II trial in advanced HCC patients who had failed prior systemic therap
Externí odkaz:
https://doaj.org/article/c84e3057b2a241b69d2832c6f9702481
Autor:
Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann
Publikováno v:
Information, Vol 12, Iss 3, p 121 (2021)
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam tim
Externí odkaz:
https://doaj.org/article/457e13c0b2a7457d9d59c9c4a105302e
Publikováno v:
PLoS ONE, Vol 13, Iss 8, p e0200822 (2018)
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. It resides on the premise of hidden capabilities-fundamental endowments underlying the productive structure. In general, measuring the ca
Externí odkaz:
https://doaj.org/article/a51b9188ce9644409249aa00e8fc809b
Publikováno v:
PLoS ONE, Vol 11, Iss 1, p e0147402 (2016)
This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two di
Externí odkaz:
https://doaj.org/article/db2daf090a874737a46a4ad1d1e01d28
Publikováno v:
Entropy, Vol 18, Iss 12, p 449 (2016)
We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer’s output, Gish and Pierce showed that uniform quantizing yields the smalles
Externí odkaz:
https://doaj.org/article/a2dfaff0f775478e88ea6b5de32493f4
GIR dataset: A geometry and real impulse response dataset for machine learning research in acoustics
Autor:
Achilleas Xydis, Nathanaël Perraudin, Romana Rust, Kurt Heutschi, Gonzalo Casas, Oksana Riba Grognuz, Kurt Eggenschwiler, Matthias Kohler, Fernando Perez-Cruz
Publikováno v:
Applied Acoustics, 208
Acoustics play a significant role in our everyday lives, influencing our communication, well-being, and perception of space. Fast and precise acoustics simulation is crucial for the accurate design of real spaces by architects and acousticians and ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::777a755a6663b1c6eccbe27a73e133e8
https://hdl.handle.net/20.500.11850/610930
https://hdl.handle.net/20.500.11850/610930
The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological backg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f345e3eb66aed301dbe4ac76c8f8e4cf
https://doi.org/10.1101/2023.05.27.542160
https://doi.org/10.1101/2023.05.27.542160
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264184
Lecture Notes in Computer Science, 13717
Machine Learning and Knowledge Discovery in Databases
Lecture Notes in Computer Science, 13717
Machine Learning and Knowledge Discovery in Databases
Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4d868d30ca64aa5194cb3f516bbfc5a
https://doi.org/10.1007/978-3-031-26419-1_11
https://doi.org/10.1007/978-3-031-26419-1_11