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pro vyhledávání: '"Verpoort, Philipp C."'
We here present an improved version of the Sortition Foundation's GROUPSELECT software package, which aims to repeatedly allocate participants of a deliberative process to discussion groups in a way that balances demographics in each group and maximi
Externí odkaz:
http://arxiv.org/abs/2410.21451
Autor:
Ueckerdt, Falko, Verpoort, Philipp C., Anantharaman, Rahul, Bauer, Christian, Beck, Fiona, Longden, Thomas, Roussanaly, Simon
Publikováno v:
In Joule 17 January 2024 8(1):104-128
Publikováno v:
Phys. Rev. B 98, 024403 (2018)
Since it was first discussed by Baxter in 1970, the three coloring model has been studied in several contexts, from frustrated magnetism to superconducting devices and glassiness. In presence of interactions, when the model is no longer exactly solub
Externí odkaz:
http://arxiv.org/abs/1802.06896
Akademický článek
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Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Sep 01. 117(36), 21857-21864.
Externí odkaz:
https://www.jstor.org/stable/26969086
On the path to climate neutrality, global patterns of industrial production and trade might change due to the heterogeneous distribution of renewable energy resources. Here we estimate the “renewables pull”, i.e. the cost savings when relocating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af36f1a9c8fc3ca73a073c8cc35826e7
Akademický článek
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The predictive capabilities of deep neural networks (DNNs) continue to evolve to increasingly impressive levels. However, it is still unclear how training procedures for DNNs succeed in finding parameters that produce good results for such high-dimen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af21f349cfbebd35d6d2999a7ad2cfaf
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America, National Academy of Sciences, 2020, 117 (36), pp.21857-21864. ⟨10.1073/pnas.1919995117⟩
Proc Natl Acad Sci U S A
Proceedings of the National Academy of Sciences of the United States of America, National Academy of Sciences, 2020, 117 (36), pp.21857-21864. ⟨10.1073/pnas.1919995117⟩
Proc Natl Acad Sci U S A
The predictive capabilities of deep neural networks (DNNs) continue to evolve to increasingly impressive levels. However, it is still unclear how training procedures for DNNs succeed in finding parameters that produce good results for such high-dimen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6971218db4bb172a58f05cbaffb3f638
https://doi.org/10.1073/pnas.1919995117
https://doi.org/10.1073/pnas.1919995117