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pro vyhledávání: '"McNamara, Declan"'
For training an encoder network to perform amortized variational inference, the Kullback-Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusive or forward KL, is an increasingly popular choice of variational obj
Externí odkaz:
http://arxiv.org/abs/2403.10610
Amortized variational inference is an often employed framework in simulation-based inference that produces a posterior approximation that can be rapidly computed given any new observation. Unfortunately, there are few guarantees about the quality of
Externí odkaz:
http://arxiv.org/abs/2305.14275
Akademický článek
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Amortized variational inference produces a posterior approximator that can compute a posterior approximation given any new observation. Unfortunately, there are few guarantees about the quality of these approximate posteriors. We propose Conformalize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcbdef746efc648223246bba1786f2b0
http://arxiv.org/abs/2305.14275
http://arxiv.org/abs/2305.14275
Autor:
McNamara, Declan, Alveen, Patricia, Damm, Signe, Carolan, Declan, Rice, James H., Murphy, Neal, Ivanković, Alojz
Publikováno v:
In International Journal of Refractory Metals and Hard Materials September 2015 52:114-122
Publikováno v:
In Procedia Materials Science 2014 3:1810-1815
Publikováno v:
In Procedia Materials Science 2014 3:1804-1809
Autor:
Nishshanke, Maleesha M.1 (AUTHOR), Jovanović, Petar1,2 (AUTHOR) petar.jovanovic@monash.edu, Panda, Manas R.1,2 (AUTHOR), Abedin, Md. Joynul1 (AUTHOR), McNamara, Declan3 (AUTHOR), Hill, Matthew R.3,4 (AUTHOR), Bhattacharya, Joydipto5,6 (AUTHOR), Kamal, Chinnathambi5,6 (AUTHOR), Shaibani, Mahdokht1,2,7 (AUTHOR) m.e.shaibani@monash.edu, Majumder, Mainak1,2 (AUTHOR) mainak.majumder@monash.edu
Publikováno v:
Advanced Energy Materials. Nov2024, p1. 15p. 8 Illustrations.
Publikováno v:
Key Engineering Materials; November 2013, Vol. 592 Issue: 1 p137-140, 4p