Zobrazeno 1 - 10
of 256
pro vyhledávání: '"Tucker, Allan"'
Autor:
Ghoshal, Biraja, Tucker, Allan
Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approxima
Externí odkaz:
http://arxiv.org/abs/2206.07795
Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consum
Externí odkaz:
http://arxiv.org/abs/2206.08787
B-cell epitopes play a key role in stimulating B-cells, triggering the primary immune response which results in antibody production as well as the establishment of long-term immunity in the form of memory cells. Consequently, being able to accurately
Externí odkaz:
http://arxiv.org/abs/2103.11214
Publikováno v:
In Heliyon 30 January 2024 10(2)
Autor:
Ghoshal, Biraja, Tucker, Allan
Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowi
Externí odkaz:
http://arxiv.org/abs/2003.10769
Autor:
Shepperd, Martin, Guo, Yuchen, Li, Ning, Arzoky, Mahir, Capiluppi, Andrea, Counsell, Steve, Destefanis, Giuseppe, Swift, Stephen, Tucker, Allan, Yousefi, Leila
Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidenc
Externí odkaz:
http://arxiv.org/abs/1909.04436
Publikováno v:
In Proceedings of IEEE eScience 2018
Botanical specimens are shared as long-term consultable research objects in a global network of specimen repositories. Multiple specimens are generated from a shared field collection event; generated specimens are then managed individually in separat
Externí odkaz:
http://arxiv.org/abs/1809.07725
Publikováno v:
Statistics and Computing 2019, 29(5):1095-1108
Learning the structure of Bayesian networks from data is known to be a computationally challenging, NP-hard problem. The literature has long investigated how to perform structure learning from data containing large numbers of variables, following a g
Externí odkaz:
http://arxiv.org/abs/1804.08137
Publikováno v:
Journal of Climate, 2021 Mar . 34(6), 2319-2335.
Externí odkaz:
https://www.jstor.org/stable/27076213