Zobrazeno 1 - 10
of 54 656
pro vyhledávání: '"WANG, Yang"'
This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn struct
Externí odkaz:
http://arxiv.org/abs/2411.14711
Autor:
Wang, Yang, Karimi, Hassan A.
With the increasing impacts of climate change, there is a growing demand for accessible tools that can provide reliable future climate information to support planning, finance, and other decision-making applications. Large language models (LLMs), suc
Externí odkaz:
http://arxiv.org/abs/2411.13724
Autor:
Chen, Yuzong, AbouElhamayed, Ahmed F., Dai, Xilai, Wang, Yang, Andronic, Marta, Constantinides, George A., Abdelfattah, Mohamed S.
Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs thro
Externí odkaz:
http://arxiv.org/abs/2411.11745
Autor:
Peng, Long, Li, Wenbo, Guo, Jiaming, Di, Xin, Sun, Haoze, Li, Yong, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zheng-Jun
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often le
Externí odkaz:
http://arxiv.org/abs/2411.10798
Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale fac
Externí odkaz:
http://arxiv.org/abs/2411.11906
Autor:
Wu, Di, Wang, Pengkun, Zhou, Shiming, Zhang, Bochun, Yu, Liheng, Chen, Xi, Wang, Xu, Zhou, Zhengyang, Wang, Yang, Wang, Sujing, Du, Jiangfeng
Determining the atomic-level structure of crystalline solids is critically important across a wide array of scientific disciplines. The challenges associated with obtaining samples suitable for single-crystal diffraction, coupled with the limitations
Externí odkaz:
http://arxiv.org/abs/2411.06062
Autor:
Yu, Miao, Wang, Shilong, Zhang, Guibin, Mao, Junyuan, Yin, Chenlong, Liu, Qijiong, Wen, Qingsong, Wang, Kun, Wang, Yang
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry. However, how to prevent these networks from generating malicious information remains unexplor
Externí odkaz:
http://arxiv.org/abs/2410.15686
In this paper, we study the strength of convex relaxations obtained by convexification of aggregation of constraints for a set $S$ described by two bilinear bipartite equalities. Aggregation is the process of rescaling the original constraints by sca
Externí odkaz:
http://arxiv.org/abs/2410.14163