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of 4 552
pro vyhledávání: '"Kim, John"'
Autor:
Shivdikar, Kaustubh, Agostini, Nicolas Bohm, Jayaweera, Malith, Jonatan, Gilbert, Abellan, Jose L., Joshi, Ajay, Kim, John, Kaeli, David
Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Despite their effectiveness, their adoption has not been pervasive becaus
Externí odkaz:
http://arxiv.org/abs/2404.15510
Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs-at-ris
Externí odkaz:
http://arxiv.org/abs/2310.00803
Autor:
Shivdikar, Kaustubh, Bao, Yuhui, Agrawal, Rashmi, Shen, Michael, Jonatan, Gilbert, Mora, Evelio, Ingare, Alexander, Livesay, Neal, Abellán, José L., Kim, John, Joshi, Ajay, Kaeli, David
Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its
Externí odkaz:
http://arxiv.org/abs/2309.11001
Autor:
Xue, Runzhen, Han, Dengke, Yan, Mingyu, Zou, Mo, Yang, Xiaocheng, Wang, Duo, Li, Wenming, Tang, Zhimin, Kim, John, Ye, Xiaochun, Fan, Dongrui
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the
Externí odkaz:
http://arxiv.org/abs/2307.12765