Zobrazeno 1 - 10
of 12 958
pro vyhledávání: '"You, Jane"'
Publikováno v:
BMJ: British Medical Journal, 2015 Dec 01. 351
Externí odkaz:
https://www.jstor.org/stable/26523658
Autor:
Rickford, John R.
Publikováno v:
Journal of Linguistics, 1986 Sep 01. 22(2), 281-310.
Externí odkaz:
https://www.jstor.org/stable/4175843
Autor:
Huang, Yongsong, Xie, Wanqing, Li, Mingzhen, Cheng, Mingmei, Wu, Jinzhou, Wang, Weixiao, You, Jane, Liu, Xiaofeng
Publikováno v:
In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham
Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small instit
Externí odkaz:
http://arxiv.org/abs/2310.15371
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain. Recently, deep self-training presents a powerful means for UDA, i
Externí odkaz:
http://arxiv.org/abs/2208.12885
Autor:
Lewis, Anne C.
Publikováno v:
The Phi Delta Kappan, 1984 Jan 01. 65(5), 307-308.
Externí odkaz:
https://www.jstor.org/stable/20387019
Autor:
Eldridge, Sheridan Wolf
Publikováno v:
CEA Critic, 1980 Nov 01. 43(1), 39-39.
Externí odkaz:
https://www.jstor.org/stable/44376056
Autor:
Flockhart, Trine1 (AUTHOR) tfl@diis.dk
Publikováno v:
Perspectives on European Politics & Society. Sep2011, Vol. 12 Issue 3, p263-282. 20p.
Autor:
Krupp, Marguerite
Publikováno v:
Technical Communication, 2012 Feb 01. 59(1), 85-85.
Externí odkaz:
https://www.jstor.org/stable/43092939
Autor:
Liu, Xiaofeng, Guo, Zhenhua, Li, Site, Xing, Fangxu, You, Jane, Kuo, C. -C. Jay, Fakhri, Georges El, Woo, Jonghye
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both $p(x|y)$ and $p(y)$. Since the label is inaccessible in the t
Externí odkaz:
http://arxiv.org/abs/2107.13469
The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discrete labels. However, in many tasks (e.g., medical diagnosis), the label
Externí odkaz:
http://arxiv.org/abs/2107.13467