Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Vincent Fortuin"'
Publikováno v:
IEEE Access, Vol 9, Pp 76750-76758 (2021)
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performan
Externí odkaz:
https://doaj.org/article/17301ebe09ad40fcaa8ede50eda636c1
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 6, p e1009086 (2021)
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of
Externí odkaz:
https://doaj.org/article/ce2ae803ad064ff398d2f8cd15512672
Autor:
Margherita Rosnati, Vincent Fortuin
Publikováno v:
PLoS ONE, Vol 16, Iss 5, p e0251248 (2021)
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. R
Externí odkaz:
https://doaj.org/article/d407b118c7dd4242942f3a4c3edda09a
Autor:
Vincent Fortuin
Publikováno v:
International Statistical Review, 90 (3)
While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cadadbc410c1182c19cbdbfa4801b5e2
Publikováno v:
CHIL
Generating interpretable visualizations of multivariate time series in the intensive care unit is of great practical importance. Clinicians seek to condense complex clinical observations into intuitively understandable critical illness patterns, like
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03e0e9a8dc3479ce0fc02de41e0a8a25
https://doi.org/10.1145/3450439.3451872
https://doi.org/10.1145/3450439.3451872
Publikováno v:
Software Impacts, 9
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance. However, it remains challenging to choose a good prior distribution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28676a0b112b18753c328490b9353857
Autor:
Margherita Rosnati, Vincent Fortuin
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 5, p e0251248 (2021)
PLoS ONE, 16 (5)
PLoS ONE, Vol 16, Iss 5, p e0251248 (2021)
PLoS ONE, 16 (5)
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c3e273ea80424a6fa75fed309f8144c
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 17, Iss 6, p e1009086 (2021)
PLoS Computational Biology, 17 (6)
PLoS Computational Biology, Vol 17, Iss 6, p e1009086 (2021)
PLoS Computational Biology, 17 (6)
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9301b6b2a7b6de93fcf8573cd5a6476c
https://doi.org/10.1371/journal.pcbi.1009086
https://doi.org/10.1371/journal.pcbi.1009086
The reconstruction of gene regulatory networks from time resolved gene expression measurements is a key challenge in systems biology with applications in health and disease. While the most popular network inference methods are based on unsupervised l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a1e29bc166270bce2bf3e64d9ed5ac2
https://doi.org/10.1101/356477
https://doi.org/10.1101/356477