Zobrazeno 1 - 10
of 56 901
pro vyhledávání: '"Li,Yong"'
Autor:
Wang, Yujie, Zhu, Shenhan, Fu, Fangcheng, Miao, Xupeng, Zhang, Jie, Zhu, Juan, Hong, Fan, Li, Yong, Cui, Bin
Recent foundation models are capable of handling multiple machine learning (ML) tasks and multiple data modalities with the unified base model structure and several specialized model components. However, the development of such multi-task (MT) multi-
Externí odkaz:
http://arxiv.org/abs/2409.03365
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-sc
Externí odkaz:
http://arxiv.org/abs/2409.01588
While deep models have proved successful in learning rich knowledge from massive well-annotated data, they may pose a privacy leakage risk in practical deployment. It is necessary to find an effective trade-off between high utility and strong privacy
Externí odkaz:
http://arxiv.org/abs/2409.02404
Human mobility prediction plays a crucial role in various real-world applications. Although deep learning based models have shown promising results over the past decade, their reliance on extensive private mobility data for training and their inabili
Externí odkaz:
http://arxiv.org/abs/2408.13986
Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors proposed many methods based on rank reduction, sparse transformation, doma
Externí odkaz:
http://arxiv.org/abs/2408.13578
Performance testing in large-scale database systems like SAP HANA is a crucial yet labor-intensive task, involving extensive manual analysis of thousands of measurements, such as CPU time and elapsed time. Manual maintenance of these metrics is time-
Externí odkaz:
http://arxiv.org/abs/2408.12414
Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion mod
Externí odkaz:
http://arxiv.org/abs/2408.10501
Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dyn
Externí odkaz:
http://arxiv.org/abs/2408.09845
Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Tradi
Externí odkaz:
http://arxiv.org/abs/2408.09825
Autor:
Gong, Jiahui, Ding, Jingtao, Meng, Fanjin, Chen, Guilong, Chen, Hong, Zhao, Shen, Lu, Haisheng, Li, Yong
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones can potentially transform into personalized assistants, anticipating us
Externí odkaz:
http://arxiv.org/abs/2408.09815