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pro vyhledávání: '"Marcus Lapeyrolerie"'
Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning
Autor:
Marcus Lapeyrolerie, Carl Boettiger
Publikováno v:
Methods in Ecology and Evolution, Vol 14, Iss 3, Pp 785-798 (2023)
Abstract In the current age of a rapidly changing environment, it is becoming increasingly important to study critical transitions and how to best anticipate them. Critical transitions pose extremely challenging forecasting problems, which necessitat
Externí odkaz:
https://doaj.org/article/91cdb626bef84ee5ac9febba47a8e125
Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning
Autor:
Marcus Lapeyrolerie, Carl Boettiger
Publikováno v:
Methods in Ecology and Evolution. 14:785-798
Publikováno v:
Philosophical Transactions of the Royal Society B: Biological Sciences. 378
From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional, and uncertain situa
Autor:
Carl Boettiger, Marcus Lapeyrolerie
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Significance Early warning signals (EWS) of tipping points are vital to anticipate system collapse or other sudden shifts. However, existing generic early warning indicators designed to work across all systems do not provide information on the state
Can machine learning help us make better decisions about a changing planet? In this paper, we illustrate and discuss the potential of a promising corner of machine learning known as _reinforcement learning_ (RL) to help tackle the most challenging co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fb6f53f0c4a869ebdfef09ee2cbfbcf
http://arxiv.org/abs/2106.08272
http://arxiv.org/abs/2106.08272
Autor:
Lapeyrolerie, Marcus, Boettiger, Carl
Publikováno v:
Methods in Ecology & Evolution; Mar2023, Vol. 14 Issue 3, p785-798, 14p
Publikováno v:
Methods in Ecology & Evolution; Nov2022, Vol. 13 Issue 11, p2649-2662, 14p