Zobrazeno 1 - 10
of 1 519
pro vyhledávání: '"LI Yukun"'
One important approach to improving the reliability of large language models (LLMs) is to provide accurate confidence estimations regarding the correctness of their answers. However, developing a well-calibrated confidence estimation model is challen
Externí odkaz:
http://arxiv.org/abs/2411.02454
This paper is concerned with developing and analyzing two novel implicit temporal discretization methods for the stochastic semilinear wave equations with multiplicative noise. The proposed methods are natural extensions of well-known time-discrete s
Externí odkaz:
http://arxiv.org/abs/2408.13134
Autor:
DeepSeek-AI, Zhu, Qihao, Guo, Daya, Shao, Zhihong, Yang, Dejian, Wang, Peiyi, Xu, Runxin, Wu, Y., Li, Yukun, Gao, Huazuo, Ma, Shirong, Zeng, Wangding, Bi, Xiao, Gu, Zihui, Xu, Hanwei, Dai, Damai, Dong, Kai, Zhang, Liyue, Piao, Yishi, Gou, Zhibin, Xie, Zhenda, Hao, Zhewen, Wang, Bingxuan, Song, Junxiao, Chen, Deli, Xie, Xin, Guan, Kang, You, Yuxiang, Liu, Aixin, Du, Qiushi, Gao, Wenjun, Lu, Xuan, Chen, Qinyu, Wang, Yaohui, Deng, Chengqi, Li, Jiashi, Zhao, Chenggang, Ruan, Chong, Luo, Fuli, Liang, Wenfeng
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoin
Externí odkaz:
http://arxiv.org/abs/2406.11931
We introduce a new concept of the locally conservative flux and investigate its relationship with the compatible discretization pioneered by Dawson, Sun and Wheeler [11]. We then demonstrate how the new concept of the locally conservative flux can pl
Externí odkaz:
http://arxiv.org/abs/2405.16117
Autor:
Li, Yukun, Liu, Liping
Diffusion models have been popular for point cloud generation tasks. Existing works utilize the forward diffusion process to convert the original point distribution into a noise distribution and then learn the reverse diffusion process to recover the
Externí odkaz:
http://arxiv.org/abs/2404.02396
Autor:
Li, Yukun, Pang, Guansong, Suo, Wei, Jing, Chenchen, Xi, Yuling, Liu, Lingqiao, Chen, Hao, Liang, Guoqiang, Wang, Peng
This paper explores the problem of continual learning (CL) of vision-language models (VLMs) in open domains, where the models need to perform continual updating and inference on a streaming of datasets from diverse seen and unseen domains with novel
Externí odkaz:
http://arxiv.org/abs/2403.10245
This paper proposes and analyzes a novel fully discrete finite element scheme with the interpolation operator for stochastic Cahn-Hilliard equations with functional-type noise. The nonlinear term satisfies a one-side Lipschitz condition and the diffu
Externí odkaz:
http://arxiv.org/abs/2306.13810
In this paper, we consider a new approach for semi-discretization in time and spatial discretization of a class of semi-linear stochastic partial differential equations (SPDEs) with multiplicative noise. The drift term of the SPDEs is only assumed to
Externí odkaz:
http://arxiv.org/abs/2303.13766
Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make une
Externí odkaz:
http://arxiv.org/abs/2302.13425
Autor:
Xie, Juan1,2 (AUTHOR), Li, Yukun3 (AUTHOR), Zeng, Tian2,4 (AUTHOR), Fan, Tingyu4 (AUTHOR), Shan, Hanguo5 (AUTHOR), Shi, Gangqing4 (AUTHOR), Zhou, Wenchao4 (AUTHOR), Zou, Juan3,4 (AUTHOR) zoujuanusc@usc.edu.cn, Lei, Xiaoyong2 (AUTHOR) leix_yong@163.com
Publikováno v:
Scientific Reports. 10/5/2024, Vol. 14 Issue 1, p1-14. 14p.
