Zobrazeno 1 - 10
of 2 505
pro vyhledávání: '"Ribeiro, António"'
The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in th
Externí odkaz:
http://arxiv.org/abs/2411.03038
Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to methods proposed in the context of deep learning, leveraging the optimizatio
Externí odkaz:
http://arxiv.org/abs/2410.12677
Publikováno v:
Educação, Vol 42, Iss 1, Pp 23-34 (2019)
Este artigo apresenta os resultados de uma pesquisa desenvolvida no Conservatório do Vale do Sousa em torno do ensino da música em regime articulado. Este trabalho pretendeu questionar o conceito de escola vocacional, testar possíveis redefiniçõ
Externí odkaz:
https://doaj.org/article/168eb0ca768647a3a02ee51434654f2a
Autor:
Habineza, Theogene, Ribeiro, Antônio H., Gedon, Daniel, Behar, Joachim A., Ribeiro, Antonio Luiz P., Schön, Thomas B.
Publikováno v:
article{HABINEZA2023193, journal = {Journal of Electrocardiology}, volume = {81}, pages = {193-200}, year = {2023}, issn = {0022-0736}}
Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure. Machine lea
Externí odkaz:
http://arxiv.org/abs/2309.16335
Autor:
Ribeiro, António Sousa
Publikováno v:
Pessoa Plural, Iss 11, Pp 3-22 (2017)
Taking the year 1915 as a reference, the essay analyses some aspects of the relationship of modernist writing with the Great War. In Portugal, this relationship appears in many cases in the form of an absence whose scattered traces can only be appreh
Externí odkaz:
https://doaj.org/article/bf794c613d3645818e9d0d5a9f2bd68a
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the
Externí odkaz:
http://arxiv.org/abs/2310.10807
Autor:
Sau, Arunashis, Pastika, Libor, Sieliwonczyk, Ewa, Patlatzoglou, Konstantinos, Ribeiro, Antônio H, McGurk, Kathryn A, Zeidaabadi, Boroumand, Zhang, Henry, Macierzanka, Krzysztof, Mandic, Danilo, Sabino, Ester, Giatti, Luana, Barreto, Sandhi M, Camelo, Lidyane do Valle, Tzoulaki, Ioanna, O'Regan, Declan P, Peters, Nicholas S, Ware, James S, Ribeiro, Antonio Luiz P, Kramer, Daniel B, Waks, Jonathan W, Ng, Fu Siong *
Publikováno v:
In The Lancet Digital Health November 2024 6(11):e791-e802
Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an
Externí odkaz:
http://arxiv.org/abs/2207.06096
Autor:
Pillonetto, Gianluigi, Aravkin, Aleksandr, Gedon, Daniel, Ljung, Lennart, Ribeiro, Antônio H., Schön, Thomas B.
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties o
Externí odkaz:
http://arxiv.org/abs/2301.12832
Autor:
Von Bachmann, Philipp, Gedon, Daniel, Gustafsson, Fredrik K., Ribeiro, Antônio H., Lampa, Erik, Gustafsson, Stefan, Sundström, Johan, Schön, Thomas B.
Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasi
Externí odkaz:
http://arxiv.org/abs/2212.13890