Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Laurence, Aitchison"'
Publikováno v:
eLife, Vol 12 (2024)
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mec
Externí odkaz:
https://doaj.org/article/08dbd6104404401eaf5e610ced0ffb47
Autor:
Mrinank Sharma, Sören Mindermann, Charlie Rogers-Smith, Gavin Leech, Benedict Snodin, Janvi Ahuja, Jonas B. Sandbrink, Joshua Teperowski Monrad, George Altman, Gurpreet Dhaliwal, Lukas Finnveden, Alexander John Norman, Sebastian B. Oehm, Julia Fabienne Sandkühler, Laurence Aitchison, Tomáš Gavenčiak, Thomas Mellan, Jan Kulveit, Leonid Chindelevitch, Seth Flaxman, Yarin Gal, Swapnil Mishra, Samir Bhatt, Jan Markus Brauner
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
European governments control resurging waves of COVID-19 using nonpharmaceutical interventions. Here, the authors estimate the effectiveness of 17 interventions in Europe’s second wave, and analyse differences to the first wave as well as implicati
Externí odkaz:
https://doaj.org/article/183987d2bc5349c891601860f62e5575
Autor:
Ali Unlu, Laurence Aitchison
Publikováno v:
Entropy, Vol 23, Iss 12, p 1629 (2021)
We developed Variational Laplace for Bayesian neural networks (BNNs), which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational
Externí odkaz:
https://doaj.org/article/c78d9df94cab4b1b9001c7ce880ee52b
Autor:
Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, Peter Watson
Existing weather models are known to have poor skill over Africa, where there are regular threats of drought and floods that present significant risks to people's lives and livelihoods. Improved precipitation forecasts could help mitigate the negativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a292ea9342e46c9cfb851b4bc795c89d
https://doi.org/10.5194/egusphere-egu23-1365
https://doi.org/10.5194/egusphere-egu23-1365
High resolution rainfall projections are useful for planning for climate change [1] but are expensive to produce using physical simulations. We make novel use of a state-of-the-art generative machine learning (ML) method, diffusion models [2], to mor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88ad6bc9916cba4b222558b8c2c5836d
https://doi.org/10.5194/egusphere-egu23-14253
https://doi.org/10.5194/egusphere-egu23-14253
Autor:
Emily Vosper, Peter Watson, Lucy Harris, Andrew McRae, Raul Santos-Rodriguez, Laurence Aitchison, Dann Mitchell
Flood hazards from Tropical cyclones (TCs) are frequently the leading cause of mortality and damages. Current research indicates that TC rainfall will increase by 7 % per degree of ocean warming with a greater proportion of them being extreme . It is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0902e88b59c5b6b8217f8494dd308cff
https://doi.org/10.5194/egusphere-egu23-8325
https://doi.org/10.5194/egusphere-egu23-8325
Autor:
Laurence Aitchison, Peter E. Latham, Jean-Pascal Pfister, Jannes Jegminat, Jorge Aurelio Menendez, Alexandre Pouget
Publikováno v:
Nature Neuroscience. 24:565-571
Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab36b3917da9693a0567b2ad504c99bb
https://doi.org/10.1038/s41593-021-00809-5
https://doi.org/10.1038/s41593-021-00809-5
Autor:
Gavin Leech, Charlie Rogers-Smith, Joshua Teperowski Monrad, Jonas B. Sandbrink, Benedict Snodin, Robert Zinkov, Benjamin Rader, John S. Brownstein, Yarin Gal, Samir Bhatt, Mrinank Sharma, Sören Mindermann, Jan M. Brauner, Laurence Aitchison
Publikováno v:
Leech, G, Rogers-Smith, C, Monrad, J T, Sandbrink, J B, Snodin, B, Zinkov, R, Rader, B, Brownstein, J S, Gal, Y, Bhatt, S, Sharma, M, Mindermann, S, Brauner, J M & Aitchison, L 2022, ' Mask wearing in community settings reduces SARS-CoV-2 transmission ', Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 23, e2119266119 . https://doi.org/10.1073/pnas.2119266119
Leech, G, Rogers-Smith, C, Monrad, J T, Sandbrink, J B, Snodin, B, Zinkov, R, Rader, B, Brownstein, J S, Gal, Y, Bhatt, S, Sharma, M, Mindermann, S, Brauner, J M & Aitchison, L 2022, ' Mask wearing in community settings reduces SARS-CoV-2 transmission ', Proceedings of the National Academy of Sciences, vol. 119, no. 23, e2119266119 . https://doi.org/10.1073/pnas.2119266119
Leech, G, Rogers-Smith, C, Monrad, J T, Sandbrink, J B, Snodin, B, Zinkov, R, Rader, B, Brownstein, J S, Gal, Y, Bhatt, S, Sharma, M, Mindermann, S, Brauner, J M & Aitchison, L 2022, ' Mask wearing in community settings reduces SARS-CoV-2 transmission ', Proceedings of the National Academy of Sciences, vol. 119, no. 23, e2119266119 . https://doi.org/10.1073/pnas.2119266119
The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al.,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f913192479ac616dda809fa8e375d53e
https://doi.org/10.1073/pnas.2119266119
https://doi.org/10.1073/pnas.2119266119
Autor:
Laurence Aitchison, Máté Lengyel
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 12, p e1005186 (2016)
Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activ
Externí odkaz:
https://doaj.org/article/533bc40a72e04380a6861124eb188923
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 12, p e1005110 (2016)
Zipf's law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf's law in each of them, those explanations are typically domain specific
Externí odkaz:
https://doaj.org/article/4c2d12ce18ee4da49aabd0cbe57df691