Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Greenbury A"'
Publikováno v:
International Conference on AI and the Digital Economy (CADE 2023), 2023, pp. 31-40
The sharing of public key information is central to the digital credential security model, but the existing Web PKI with its opaque Certification Authorities and synthetic attestations serves a very different purpose. We propose a new approach to dec
Externí odkaz:
http://arxiv.org/abs/2305.08533
Autor:
Abroshan, Mahed, Burkhart, Michael, Giles, Oscar, Greenbury, Sam, Kourtzi, Zoe, Roberts, Jack, van der Schaar, Mihaela, Steyn, Jannetta S, Wilson, Alan, Yong, May
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems often stop at the point of publication, proof of concept or when made acc
Externí odkaz:
http://arxiv.org/abs/2303.01513
Autor:
Mathias Cramm, Theresa Frei, Aida Greenbury, Georg Winkel, Yitagesu Tekle Tegegne, Metodi Sotirov
Publikováno v:
Heliyon, Vol 10, Iss 9, Pp e30732- (2024)
This paper addresses knowledge gaps related to implementation of corporate zero deforestation commitments (ZDCs). Drawing on an analytical framework of organizational change, we scrutinize changes and processes internal to a company in adjusting to z
Externí odkaz:
https://doaj.org/article/8af6cf88e43444b792269d4de6157ea5
Autor:
Cramm, Mathias, Frei, Theresa, Greenbury, Aida, Winkel, Georg, Tegegne, Yitagesu Tekle, Sotirov, Metodi
Publikováno v:
In Heliyon 15 May 2024 10(9)
Autor:
Peach, Robert L., Greenbury, Sam F., Johnston, Iain G., Yaliraki, Sophia N., Lefevre, David, Barahona, Mauricio
The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completio
Externí odkaz:
http://arxiv.org/abs/2007.07003
The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a h
Externí odkaz:
http://arxiv.org/abs/1912.00762
Akademický článek
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Publikováno v:
PLoS Comput Biol 12(3): e1004773. (2016)
Mutational neighbourhoods in genotype-phenotype (GP) maps are widely believed to be more likely to share characteristics than expected from random chance. Such genetic correlations should, as John Maynard Smith famously pointed out, strongly influenc
Externí odkaz:
http://arxiv.org/abs/1505.07821
Autor:
Sam F. Greenbury, Kayleigh Ougham, Jinyi Wu, Cheryl Battersby, Chris Gale, Neena Modi, Elsa D. Angelini
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in
Externí odkaz:
https://doaj.org/article/57c4b1daaf4645168fb16db8a186d777
Autor:
Robert L. Peach, Sam F. Greenbury, Iain G. Johnston, Sophia N. Yaliraki, David J. Lefevre, Mauricio Barahona
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task
Externí odkaz:
https://doaj.org/article/feaf2b62a3224a24ab940ed48afc5c8b