Zobrazeno 1 - 10
of 5 134
pro vyhledávání: '"Lu , Nan"'
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead electrical
Externí odkaz:
http://arxiv.org/abs/2403.03898
Publikováno v:
BMC Cancer, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background Several inflammatory indicators have been reported to have predictive value in many types of malignant cancer. This research was aimed to explore the ability of the monocyte-to-lymphocyte ratio (MLR) to predict prognosis in patien
Externí odkaz:
https://doaj.org/article/6d5ef808ea654912bd8f3a7ab9884609
Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature on corruption predominantly focuses on specific settings and learning scenarios, lacking a unified view. There is still a limited understandi
Externí odkaz:
http://arxiv.org/abs/2307.08643
Distribution shift (DS) may have two levels: the distribution itself changes, and the support (i.e., the set where the probability density is non-zero) also changes. When considering the support change between the training and test distributions, the
Externí odkaz:
http://arxiv.org/abs/2305.14690
Autor:
Lu, Nan1,2,3 (AUTHOR), Jiang, Qiming1,3,4,5 (AUTHOR), Xu, Tianshu1,3,4,6 (AUTHOR), Gao, Qiyuan1,2,3 (AUTHOR), Wang, Yuepeng1,3,4 (AUTHOR), Huang, Zixian1,3,4 (AUTHOR) huangzx66@mail.sysu.edu.cn, Huang, Zhiquan1,3,4 (AUTHOR) hzhquan@mail.sysu.edu.cn, Xu, Xiaoding1,2,3 (AUTHOR) xuxiaod5@mail.sysu.edu.cn
Publikováno v:
Journal of Experimental & Clinical Cancer Research (17569966). 9/30/2024, Vol. 43 Issue 1, p1-15. 15p.
Recent years have witnessed a great success of supervised deep learning, where predictive models were trained from a large amount of fully labeled data. However, in practice, labeling such big data can be very costly and may not even be possible for
Externí odkaz:
http://arxiv.org/abs/2207.01555
Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practi
Externí odkaz:
http://arxiv.org/abs/2204.03304
Autor:
Apresyan, Artur, Diaz, Daniel, Duarte, Javier, Ganguly, Sanmay, Kansal, Raghav, Lu, Nan, Suarez, Cristina Mantilla, Mukherjee, Samadrita, Peña, Cristían, Sheldon, Brian, Xie, Si
One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs sector. This includes a detailed study of Higgs boson (H) pair producti
Externí odkaz:
http://arxiv.org/abs/2203.07353