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pro vyhledávání: '"DUMONTIER, Michel"'
Audio-language models (ALMs) process sounds to provide a linguistic description of sound-producing events and scenes. Recent advances in computing power and dataset creation have led to significant progress in this domain. This paper surveys existing
Externí odkaz:
http://arxiv.org/abs/2407.06947
Autor:
Ibrahim, Mahmoud, Khalil, Yasmina Al, Amirrajab, Sina, Sun, Chang, Breeuwer, Marcel, Pluim, Josien, Elen, Bart, Ertaylan, Gokhan, Dumontier, Michel
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, an
Externí odkaz:
http://arxiv.org/abs/2407.00116
Rule mining on knowledge graphs allows for explainable link prediction. Contrarily, embedding-based methods for link prediction are well known for their generalization capabilities, but their predictions are not interpretable. Several approaches comb
Externí odkaz:
http://arxiv.org/abs/2406.10144
Automated Audio Captioning is a multimodal task that aims to convert audio content into natural language. The assessment of audio captioning systems is typically based on quantitative metrics applied to text data. Previous studies have employed metri
Externí odkaz:
http://arxiv.org/abs/2403.18572
In many countries financial service providers have to elicit their customers risk preferences, when offering products and services. For instance, in the Netherlands pension funds will be legally obliged to factor in their clients risk preferences whe
Externí odkaz:
http://arxiv.org/abs/2311.04164
Arguments for the FAIR principles have mostly been based on appeals to values. However, the work of onboarding diverse researchers to make efficient and effective implementations of FAIR requires different appeals. In our recent effort to transform t
Externí odkaz:
http://arxiv.org/abs/2303.07429
Autor:
Urovi, Visara, Celebi, Remzi, Sun, Chang, Rieswijk, Linda, Erard, Michael, Yilmaz, Arif, Moodley, Kody, Kumar, Parveen, Dumontier, Michel
Data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set in these f
Externí odkaz:
http://arxiv.org/abs/2302.01041
Publikováno v:
Journal of Medical Internet Research, Vol 17, Iss 3, p e80 (2015)
BackgroundThere is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement
Externí odkaz:
https://doaj.org/article/dcd3a8751e394b24b6944b207ffe2fc8
Despite the remarkable success of Generative Adversarial Networks (GANs) on text, images, and videos, generating high-quality tabular data is still under development owing to some unique challenges such as capturing dependencies in imbalanced data, o
Externí odkaz:
http://arxiv.org/abs/2206.13787
Autor:
Unni, Deepak R., Moxon, Sierra A. T., Bada, Michael, Brush, Matthew, Bruskiewich, Richard, Clemons, Paul, Dancik, Vlado, Dumontier, Michel, Fecho, Karamarie, Glusman, Gustavo, Hadlock, Jennifer J., Harris, Nomi L., Joshi, Arpita, Putman, Tim, Qin, Guangrong, Ramsey, Stephen A., Shefchek, Kent A., Solbrig, Harold, Soman, Karthik, Thessen, Anne T., Haendel, Melissa A., Bizon, Chris, Mungall, Christopher J., Consortium, the Biomedical Data Translator
Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structur
Externí odkaz:
http://arxiv.org/abs/2203.13906