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pro vyhledávání: '"Henry B. Moss"'
Reaction additives play a significant role in controlling the reactivity and outcomes of chemical reactions. For example, a recent high-throughput additive screening identified a phthalimide ligand additive for Ni-catalysed photoredox decarboxylative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b466a9a743d262d1b8d0bc126e62c43
https://doi.org/10.26434/chemrxiv-2022-nll2j-v2
https://doi.org/10.26434/chemrxiv-2022-nll2j-v2
Autor:
Ryan-Rhys Griffiths, Jake L. Greenfield, Aditya R. Thawani, Arian R. Jamasb, Henry B. Moss, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A. Aldrick, Matthew J. Fuchter, Alpha A. Lee
Publikováno v:
Chemical science. 13(45)
Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states wh
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676636
ECML/PKDD (3)
ECML/PKDD (3)
We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3bb3af21fef2f0c5d8870b174f4b1ae
https://doi.org/10.1007/978-3-030-67664-3_27
https://doi.org/10.1007/978-3-030-67664-3_27
Publikováno v:
ICASSP
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target uttera
Publikováno v:
ACL (1)
Web of Science
Web of Science
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comp