Zobrazeno 1 - 10
of 22 242
pro vyhledávání: '"Zhenguo An"'
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10370 (2024)
Due to the high sensitivity of grid-based micro-scale wind–sand flow models to deformation and distortion, this study employs the Smooth Particle Hydrodynamics (SPH) method for numerical simulations. The advantage of the SPH method is that it can d
Externí odkaz:
https://doaj.org/article/dec66b01135d4f14b377da8edcb3b73a
Autor:
Chen, Guoxuan, Shi, Han, Li, Jiawei, Gao, Yihang, Ren, Xiaozhe, Chen, Yimeng, Jiang, Xin, Li, Zhenguo, Liu, Weiyang, Huang, Chao
Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due
Externí odkaz:
http://arxiv.org/abs/2412.12094
Autor:
Pan, Haolin, Li, Han, Huang, Jixiang, Liu, Zheng, Fang, Mingyue, Yuan, Yanan, Liu, Daxiang, Hu, Xintong, Peng, Wenzhi, Liang, Zhenguo, Chang, Xiao, Sheng, Zhigao, Chen, Xianzhe, Wang, Lingfei, Li, Qian, Li, Peng, Niu, Qian, Gao, Yang, Yang, Qinghui, Hou, Dazhi
The Magneto-Optical Kerr Effect (MOKE) is a fundamental tool in magnetometry, pivotal for advancing research in optics, magnetism, and spintronics as a direct probe of magnetization. Traditional MOKE measurements primarily detect the magnetization co
Externí odkaz:
http://arxiv.org/abs/2412.09857
Autor:
Li, Kaican, Xie, Weiyan, Huang, Yongxiang, Deng, Didan, Hong, Lanqing, Li, Zhenguo, Silva, Ricardo, Zhang, Nevin L.
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. However, not all pre-trained features are robust and those methods are
Externí odkaz:
http://arxiv.org/abs/2411.19757
Autor:
Huang, Minbin, Huang, Runhui, Shi, Han, Chen, Yimeng, Zheng, Chuanyang, Sun, Xiangguo, Jiang, Xin, Li, Zhenguo, Cheng, Hong
The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering an
Externí odkaz:
http://arxiv.org/abs/2411.17773
The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is essential for applications like autonomous driving. However, existing methods are limited by scalability and how con
Externí odkaz:
http://arxiv.org/abs/2411.13807
Autor:
Wei, Zhenguo, Zhang, Hao
This article is devoted to the study of the Schatten class membership of commutators involving singular integral operators. We utilize martingale paraproducts and Hyt\"{o}nen's dyadic martingale technique to obtain sufficient conditions on the weak-t
Externí odkaz:
http://arxiv.org/abs/2411.05810
Autor:
Li, Qintong, Gao, Jiahui, Wang, Sheng, Pi, Renjie, Zhao, Xueliang, Wu, Chuan, Jiang, Xin, Li, Zhenguo, Kong, Lingpeng
Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream applications. Current methods often rely on human-annotated data or pr
Externí odkaz:
http://arxiv.org/abs/2410.16736
Autor:
Ye, Jiacheng, Gao, Jiahui, Gong, Shansan, Zheng, Lin, Jiang, Xin, Li, Zhenguo, Kong, Lingpeng
Autoregressive language models, despite their impressive capabilities, struggle with complex reasoning and long-term planning tasks. We introduce discrete diffusion models as a novel solution to these challenges. Through the lens of subgoal imbalance
Externí odkaz:
http://arxiv.org/abs/2410.14157
Autor:
Feng, Guhao, Yang, Kai, Gu, Yuntian, Ai, Xinyue, Luo, Shengjie, Sun, Jiacheng, He, Di, Li, Zhenguo, Wang, Liwei
Despite the remarkable success of Transformer-based Large Language Models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a significant challenge. In this paper, we conduct a rigorous theoretical ana
Externí odkaz:
http://arxiv.org/abs/2410.13857