Zobrazeno 1 - 10
of 10 630
pro vyhledávání: '"An, Zidong"'
To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuo
Externí odkaz:
http://arxiv.org/abs/2412.13912
Large language models (LLMs) possess extensive knowledge and question-answering capabilities, having been widely deployed in privacy-sensitive domains like finance and medical consultation. During LLM inferences, cache-sharing methods are commonly em
Externí odkaz:
http://arxiv.org/abs/2411.18191
The application of intelligent decision-making in unmanned aerial vehicle (UAV) is increasing, and with the development of UAV 1v1 pursuit-evasion game, multi-UAV cooperative game has emerged as a new challenge. This paper proposes a deep reinforceme
Externí odkaz:
http://arxiv.org/abs/2411.02983
Autor:
Wang, Qi, Ren, Pu, Zhou, Hao, Liu, Xin-Yang, Deng, Zhiwen, Zhang, Yi, Chengze, Ruizhi, Liu, Hongsheng, Wang, Zidong, Wang, Jian-Xun, Ji-Rong_Wen, Sun, Hao, Liu, Yang
When solving partial differential equations (PDEs), classical numerical methods often require fine mesh grids and small time stepping to meet stability, consistency, and convergence conditions, leading to high computational cost. Recently, machine le
Externí odkaz:
http://arxiv.org/abs/2411.00040
This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks
Externí odkaz:
http://arxiv.org/abs/2410.20203
\textit{Nature is infinitely resolution-free}. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing image resolutions outside of their trained domain. To address this limitat
Externí odkaz:
http://arxiv.org/abs/2410.13925
Autor:
Yue, Xiaoyu, Wang, Zidong, Lu, Zeyu, Sun, Shuyang, Wei, Meng, Ouyang, Wanli, Bai, Lei, Zhou, Luping
Conventional class-guided diffusion models generally succeed in generating images with correct semantic content, but often struggle with texture details. This limitation stems from the usage of class priors, which only provide coarse and limited cond
Externí odkaz:
http://arxiv.org/abs/2410.08531
Autor:
Zeng, Bocheng, Wang, Qi, Yan, Mengtao, Liu, Yang, Chengze, Ruizhi, Zhang, Yi, Liu, Hongsheng, Wang, Zidong, Sun, Hao
Solving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotemporal dynamics (e.g., tremendo
Externí odkaz:
http://arxiv.org/abs/2410.01337
Autor:
Qin, Chengxuan, Yang, Rui, You, Wenlong, Chen, Zhige, Zhu, Longsheng, Huang, Mengjie, Wang, Zidong
The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG dat
Externí odkaz:
http://arxiv.org/abs/2410.07196
Autor:
Yu, Zhongkai, Liang, Shengwen, Ma, Tianyun, Cai, Yunke, Nan, Ziyuan, Huang, Di, Song, Xinkai, Hao, Yifan, Zhang, Jie, Zhi, Tian, Zhao, Yongwei, Du, Zidong, Hu, Xing, Guo, Qi, Chen, Tianshi
Publikováno v:
MICRO 2024
Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience while preserving intelligent capabilities. However, such a task exhibit
Externí odkaz:
http://arxiv.org/abs/2409.15654