Zobrazeno 1 - 10
of 749
pro vyhledávání: '"Löfstedt P"'
Predicting future interactions or novel links in networks is an indispensable tool across diverse domains, including genetic research, online social networks, and recommendation systems. Among the numerous techniques developed for link prediction, th
Externí odkaz:
http://arxiv.org/abs/2410.08777
Considering the growing prominence of production-level AI and the threat of adversarial attacks that can evade a model at run-time, evaluating the robustness of models to these evasion attacks is of critical importance. Additionally, testing model ch
Externí odkaz:
http://arxiv.org/abs/2409.07609
Advancements in science rely on data sharing. In medicine, where personal data are often involved, synthetic tabular data generated by generative adversarial networks (GANs) offer a promising avenue. However, existing GANs struggle to capture the com
Externí odkaz:
http://arxiv.org/abs/2405.16971
Multi-object grasping is a challenging task. It is important for energy and cost-efficient operation of industrial crane manipulators, such as those used to collect tree logs off the forest floor and onto forest machines. In this work, we used synthe
Externí odkaz:
http://arxiv.org/abs/2403.11623
Machine learning models -- deep neural networks in particular -- have performed remarkably well on benchmark datasets across a wide variety of domains. However, the ease of finding adversarial counter-examples remains a persistent problem when traini
Externí odkaz:
http://arxiv.org/abs/2401.13751
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard DA produces synthetic data for augmentation with limited diversity. Gen
Externí odkaz:
http://arxiv.org/abs/2307.11375
Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducib
Externí odkaz:
http://arxiv.org/abs/2210.11146
Autor:
Attila Simkó, Mikael Bylund, Gustav Jönsson, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 270-277 (2024)
The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily
Externí odkaz:
https://doaj.org/article/f8b8eba5218f4a42988397a4996bad01
Autor:
Kenisha Russell Jonsson, Cameron Kymani Bailey, Maria Corell, Petra Löfstedt, Nicholas Kofi Adjei
Publikováno v:
Child and Adolescent Psychiatry and Mental Health, Vol 18, Iss 1, Pp 1-11 (2024)
Abstract Aims This study aims to investigate the association between dietary behaviours, overweight/obesity, and mental health and well-being among Swedish adolescents. Methods Data from the 2017/2018 Health Behaviour in School-aged Children (HBSC) s
Externí odkaz:
https://doaj.org/article/af980091377f4b498cc5959fbd7e8109
Publikováno v:
Archives of Public Health, Vol 82, Iss 1, Pp 1-12 (2024)
Abstract Background Adolescents in Sweden experience more mental health problems and lower mental well-being than adolescents in other Nordic countries. According to the literature, one possible explanation may be differences in income inequality. Th
Externí odkaz:
https://doaj.org/article/e9a154d722ac4aca8626e97a3d52316e