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pro vyhledávání: '"Liu, Denis"'
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is better harnessed when the models are coupled with external verifiers
Externí odkaz:
http://arxiv.org/abs/2402.04210
Autor:
Liu, Denis
Generative adversarial networks (GANs) have recently become a popular data augmentation technique used by machine learning practitioners. However, they have been shown to suffer from the so-called mode collapse failure mode, which makes them vulnerab
Externí odkaz:
http://arxiv.org/abs/2308.13554
Autor:
Wei, Xiaolong, Yang, Jiekun, Adair, Sara J., Ozturk, Harun, Kuscu, Cem, Lee, Kyung Yong, Kane, William J., O’Hara, Patrick E., Liu, Denis, Demirlenk, Yusuf Mert, Habieb, Alaa Hamdi, Yilmaz, Ebru, Dutta, Anindya, Bauer, Todd W., Adli, Mazhar
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Nov 01. 117(45), 28068-28079.
Externí odkaz:
https://www.jstor.org/stable/26970706
Autor:
Xiaolong Wei, Jiekun Yang, Adair, Sara J., Ozturk, Harun, Kuscu, Cem, Kyung Yong Lee, Kane, William J., O'Hara, Patrick E., Liu, Denis, Demirlenk, Yusuf Mert, Habieb, Alaa Hamdi, Yilmaz, Ebru, Dutta, Anindya, Bauer, Todd W., Adli, Mazhar
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America; 11/10/2020, Vol. 117 Issue 45, p28068-28079, 12p
Akademický článek
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