Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Ribeiro, Antonio H."'
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
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:
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:
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
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
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 such examples. It is formulated as a min-max problem, sea
Externí odkaz:
http://arxiv.org/abs/2205.12695
Autor:
Ribeiro, Antônio H., Schön, Thomas B.
We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the prediction error. We provide bounds on the prediction error in the presence
Externí odkaz:
http://arxiv.org/abs/2204.06274
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
