Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Shandian Zhe"'
Autor:
Zheng Wang, Shandian Zhe, Joshua Zimmerman, Candice Morrisey, Joseph E. Tonna, Vikas Sharma, Ryan A. Metcalf
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract Accurately predicting red blood cell (RBC) transfusion requirements in cardiothoracic (CT) surgery could improve blood inventory management and be used as a surrogate marker for assessing hemorrhage risk preoperatively. We developed a machin
Externí odkaz:
https://doaj.org/article/aabcf48403ea4a1f9a3f7be4b158da8e
Challenges in multi-fidelity modeling relate to accuracy, uncertainty estimation and high-dimensionality. A novel additive structure is introduced in which the highest fidelity solution is written as a sum of the lowest fidelity solution and residual
Externí odkaz:
http://arxiv.org/abs/2104.03743
Autor:
Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha, Aidan Good, Ruisen Tu, Xin Yu, Shandian Zhe, Thiago Serra
Publikováno v:
Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783031332708
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41829de342a7edc4fa81cc046b605515
https://doi.org/10.1007/978-3-031-33271-5_14
https://doi.org/10.1007/978-3-031-33271-5_14
Publikováno v:
Applied Mathematical Modelling. 97:36-56
Challenges in multi-fidelity modelling relate to accuracy, uncertainty estimation and high-dimensionality. A novel additive structure is introduced in which the highest fidelity solution is written as a sum of the lowest fidelity solution and residua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::307570ee235cb87764913e298b467f19
https://doi.org/10.1016/j.apm.2021.03.041
https://doi.org/10.1016/j.apm.2021.03.041
Publikováno v:
Transportation Research Part B: Methodological. 146:88-110
Despite the wide implementation of machine learning (ML) technique in traffic flow modeling recently, those data-driven approaches often fall short of accuracy in the cases with a small or noisy training dataset. To address this issue, this study pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5cb5c52e32841b96525c0d361341cb9
https://doi.org/10.1016/j.trb.2021.02.007
https://doi.org/10.1016/j.trb.2021.02.007
Publikováno v:
Neural Networks. 130:11-21
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good performance, the huge number of parameters in DNNs brings challenges to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c20fdb92eb43f817dce54773de568d29
https://doi.org/10.1016/j.neunet.2020.05.034
https://doi.org/10.1016/j.neunet.2020.05.034
Publikováno v:
AAAI
Learning compact representations for shapes (binary images) is important for many applications. Although neural network models are very powerful, they usually involve many parameters, require substantial tuning efforts and easily overfit small datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3784d7ddae313764c30ac3f1742dae5
https://doi.org/10.1609/aaai.v34i04.6118
https://doi.org/10.1609/aaai.v34i04.6118
Publikováno v:
Neural Computing and Applications. 32:8187-8204
Recently, low-rank and sparse representation-based methods have achieved great success in subspace clustering, which aims to cluster data lying in a union of subspaces. However, most methods fail if the data samples are corrupted by noise and outlier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::609f9c23d42bc965ca52b49898eaa871
https://doi.org/10.1007/s00521-019-04317-3
https://doi.org/10.1007/s00521-019-04317-3
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 30:318-324
Link prediction is a fundamental problem in network modeling. A family of link prediction approaches is to treat network data as an exchangeable array whose entries can be explained by random functions (e.g., block models and Gaussian processes) over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cd46662a26ecf08884df38aa0ab4877
https://doi.org/10.1109/tnnls.2018.2837667
https://doi.org/10.1109/tnnls.2018.2837667
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eed5ffca39e6e5b017c4ad63d14e620a
https://doi.org/10.2139/ssrn.3965238
https://doi.org/10.2139/ssrn.3965238