Zobrazeno 1 - 10
of 644
pro vyhledávání: '"Chen Yuzhong"'
Publikováno v:
National Science Open, Vol 2 (2023)
Recently, two-dimensional magnetic materials (2DMMs) have become a focused research direction in a broad range of two-dimensional materials, due to their underlying significance in fundamental research, as well as in technologically relevant applicat
Externí odkaz:
https://doaj.org/article/60230c68c44d46c59baefa1088f47a59
Textual information of data is of vital importance for data mining and feature engineering. However, existing methods focus on learning the data structures and overlook the textual information along with the data. Consequently, they waste this valuab
Externí odkaz:
http://arxiv.org/abs/2406.11177
Autor:
Xu, Zhe, Qiu, Ruizhong, Chen, Yuzhong, Chen, Huiyuan, Fan, Xiran, Pan, Menghai, Zeng, Zhichen, Das, Mahashweta, Tong, Hanghang
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks. Overall, accordin
Externí odkaz:
http://arxiv.org/abs/2405.11416
Autor:
Fang, Irving, Chen, Yuzhong, Wang, Yifan, Zhang, Jianghan, Zhang, Qiushi, Xu, Jiali, He, Xibo, Gao, Weibo, Su, Hao, Li, Yiming, Feng, Chen
A robot's ability to anticipate the 3D action target location of a hand's movement from egocentric videos can greatly improve safety and efficiency in human-robot interaction (HRI). While previous research predominantly focused on semantic action cla
Externí odkaz:
http://arxiv.org/abs/2403.05046
Publikováno v:
Nanotechnology Reviews, Vol 7, Iss 5, Pp 443-468 (2018)
As memristor-simulating synaptic devices have become available in recent years, the optimization on non-linearity degree (NL, related to adjacent conductance values) is unignorable in the promotion of the learning accuracy of systems. Importantly, ba
Externí odkaz:
https://doaj.org/article/274c01f8d0b7450aae459064c855b101
Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking. This research, bridging computer science and urban studies, addresses the challenge of integrating these fields with their di
Externí odkaz:
http://arxiv.org/abs/2402.15398
Autor:
Zhao, Yuying, Xu, Minghua, Chen, Huiyuan, Chen, Yuzhong, Cai, Yiwei, Islam, Rashidul, Wang, Yu, Derr, Tyler
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced nature of users' interests, which span a wide range of diversity, pose
Externí odkaz:
http://arxiv.org/abs/2402.13495
Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning fra
Externí odkaz:
http://arxiv.org/abs/2401.15874
Autor:
Xu, Zhe, Pan, Menghai, Chen, Yuzhong, Chen, Huiyuan, Yan, Yuchen, Das, Mahashweta, Tong, Hanghang
Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In graph machine learning context, graph rationale is defined to locate the critical subgraph in the given graph topology
Externí odkaz:
http://arxiv.org/abs/2312.07859
Autor:
Lai, Kwei-Herng, Zha, Daochen, Chen, Huiyuan, Bendre, Mangesh, Chen, Yuzhong, Das, Mahashweta, Yang, Hao, Hu, Xia
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally diffic
Externí odkaz:
http://arxiv.org/abs/2308.14838