Zobrazeno 1 - 10
of 1 897
pro vyhledávání: '"Lanning, P"'
Link prediction is a fundamental task in graph learning, inherently shaped by the topology of the graph. While traditional heuristics are grounded in graph topology, they encounter challenges in generalizing across diverse graphs. Recent research eff
Externí odkaz:
http://arxiv.org/abs/2406.07979
Autor:
Duan, Xiaohui, Li, Yuxuan, Liu, Zhao, Yang, Bin, Zheng, Juepeng, Fu, Haohuan, Zhang, Shaoqing, Xu, Shiming, Gao, Yang, Xue, Wei, Wei, Di, Lv, Xiaojing, Yan, Lifeng, Huang, Haopeng, Lu, Haitian, Wan, Lingfeng, Lin, Haoran, Chang, Qixin, Li, Chenlin, He, Quanjie, Song, Zeyu, Wang, Xuantong, Yu, Yangyang, Fan, Xilong, Qu, Zhaopeng, Xu, Yankun, Guo, Xiuwen, Fei, Yunlong, Wang, Zhaoying, Li, Mingkui, Jiang, Yingjing, Lu, Lv, Su, Liang, Fu, Jiayu, Yu, Peinan, Liu, Weiguo, Wu, Lixin, Wang, Lanning, Liu, Xin, Chen, Dexun, Yang, Guangwen
With current and future leading systems adopting heterogeneous architectures, adapting existing models for heterogeneous supercomputers is of urgent need for improving model resolution and reducing modeling uncertainty. This paper presents our three-
Externí odkaz:
http://arxiv.org/abs/2404.10253
Interferometric telescopes are instrumental for the imaging of distant astronomical bodies, but optical loss heavily restricts how far telescopes in an array can be placed from one another, leading to a bottleneck in the resolution that can be achiev
Externí odkaz:
http://arxiv.org/abs/2403.03491
Graph-structured data are the commonly used and have wide application scenarios in the real world. For these diverse applications, the vast variety of learning tasks, graph domains, and complex graph learning procedures present challenges for human e
Externí odkaz:
http://arxiv.org/abs/2402.11641
Publikováno v:
Advances in Medical Education and Practice, Vol Volume 15, Pp 1059-1067 (2024)
Stephanie Stroever,1 Colten Lanning,2 Miloš Buhavac,3 Cameran Mecham,4 Andrea Weitz,4 Frank Frankovsky,2 Andres Rios,2 James Morris5 1Department of Medical Education, Texas Tech University Health Sciences Center School of Medicine, Lubbock, TX, USA;
Externí odkaz:
https://doaj.org/article/ab8b7e61223844119c296ca40d0e6b50
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve this target through automated machine learning techniques, pre-traini
Externí odkaz:
http://arxiv.org/abs/2309.04565
Autor:
Lafler, Randy, Eickhoff, Mark L., Newey, Scott C., Gonzalez, Yamil Nieves, Stoltenburg, Kurt E., Camacho, J. Frank, Harris, Mark A., Oesch, Denis W., Lewis, Adrian J., Lanning, R. Nicholas
High-precision remote clock synchronization is crucial for many classical and quantum network applications. Evaluating options for space-Earth links, we find that traditional solutions may not produce the desired synchronization for low Earth orbits
Externí odkaz:
http://arxiv.org/abs/2307.07371
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are sub-optimal without utilizing
Externí odkaz:
http://arxiv.org/abs/2302.08671
Autor:
Michela Carter, Samuel C Linton, Suhail Zeineddin, J Benjamin Pitt, Christopher De Boer, Angie Figueroa, Ankush Gosain, David Lanning, Aaron Lesher, Saleem Islam, Chethan Sathya, Jane L Holl, Hassan MK Ghomrawi, Fizan Abdullah
Publikováno v:
JMIR Perioperative Medicine, Vol 7, p e58663 (2024)
BackgroundAt present, parents lack objective methods to evaluate their child’s postoperative recovery following discharge from the hospital. As a result, clinicians are dependent upon a parent’s subjective assessment of the child’s health statu
Externí odkaz:
https://doaj.org/article/c248d987591349afac4e02f00cc8e7d8
In recent years, Graph Neural Networks (GNNs) have been popular in graph representation learning which assumes the homophily property, i.e., the connected nodes have the same label or have similar features. However, they may fail to generalize into t
Externí odkaz:
http://arxiv.org/abs/2211.10990