Zobrazeno 1 - 10
of 92 022
pro vyhledávání: '"An, Jiaqi"'
Autor:
Zhong, Tianyang, Liu, Zhengliang, Pan, Yi, Zhang, Yutong, Zhou, Yifan, Liang, Shizhe, Wu, Zihao, Lyu, Yanjun, Shu, Peng, Yu, Xiaowei, Cao, Chao, Jiang, Hanqi, Chen, Hanxu, Li, Yiwei, Chen, Junhao, Hu, Huawen, Liu, Yihen, Zhao, Huaqin, Xu, Shaochen, Dai, Haixing, Zhao, Lin, Zhang, Ruidong, Zhao, Wei, Yang, Zhenyuan, Chen, Jingyuan, Wang, Peilong, Ruan, Wei, Wang, Hui, Zhao, Huan, Zhang, Jing, Ren, Yiming, Qin, Shihuan, Chen, Tong, Li, Jiaxi, Zidan, Arif Hassan, Jahin, Afrar, Chen, Minheng, Xia, Sichen, Holmes, Jason, Zhuang, Yan, Wang, Jiaqi, Xu, Bochen, Xia, Weiran, Yu, Jichao, Tang, Kaibo, Yang, Yaxuan, Sun, Bolun, Yang, Tao, Lu, Guoyu, Wang, Xianqiao, Chai, Lilong, Li, He, Lu, Jin, Sun, Lichao, Zhang, Xin, Ge, Bao, Hu, Xintao, Zhang, Lian, Zhou, Hua, Zhang, Lu, Zhang, Shu, Liu, Ninghao, Jiang, Bei, Kong, Linglong, Xiang, Zhen, Ren, Yudan, Liu, Jun, Jiang, Xi, Bao, Yu, Zhang, Wei, Li, Xiang, Li, Gang, Liu, Wei, Shen, Dinggang, Sikora, Andrea, Zhai, Xiaoming, Zhu, Dajiang, Liu, Tianming
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguist
Externí odkaz:
http://arxiv.org/abs/2409.18486
Autor:
Li, Jiaqi, Wang, Yiran, Zheng, Jinghong, Huang, Zihao, Xian, Ke, Cao, Zhiguo, Zhang, Jianming
Depth refinement aims to infer high-resolution depth with fine-grained edges and details, refining low-resolution results of depth estimation models. The prevailing methods adopt tile-based manners by merging numerous patches, which lacks efficiency
Externí odkaz:
http://arxiv.org/abs/2409.17880
Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkabl
Externí odkaz:
http://arxiv.org/abs/2409.17510
Understanding human mobility patterns has traditionally been a complex challenge in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across diverse locations, conventional activity-based models and learning
Externí odkaz:
http://arxiv.org/abs/2409.17495
How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence
Externí odkaz:
http://arxiv.org/abs/2409.18153
Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models are heavily burdened by comput
Externí odkaz:
http://arxiv.org/abs/2409.16675
Autor:
Zhou, Xinxing, Ye, Jiaqi, Zhao, Shubao, Jin, Ming, Hou, Zhaoxiang, Yang, Chengyi, Li, Zengxiang, Wen, Yanlong, Yuan, Xiaojie
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional forecasting methods struggle to cope with the growing complexity and variabi
Externí odkaz:
http://arxiv.org/abs/2409.15794
Autor:
Ye, Haotian, Lin, Haowei, Han, Jiaqi, Xu, Minkai, Liu, Sheng, Liang, Yitao, Ma, Jianzhu, Zou, James, Ermon, Stefano
Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. Existing methods, t
Externí odkaz:
http://arxiv.org/abs/2409.15761
The assembly of molecules with covalent networks can form varied lattice structures with distinct physical and chemical properties from conventional atomic lattices. Using the smallest stable [5,6]fullerene units as building blocks, various 2D C$_{24
Externí odkaz:
http://arxiv.org/abs/2409.15421
Autor:
Yu, Scarlett S., You, Jiaqi, Bao, Yicheng, Anderegg, Loic, Hallas, Christian, Li, Grace K., Lim, Dongkyu, Chae, Eunmi, Ketterle, Wolfgang, Ni, Kang-Kuen, Doyle, John M.
We report the experimental realization of a conveyor-belt magneto-optical trap for calcium monofluoride (CaF) molecules. The obtained highly-compressed cloud has a mean radius of 64(5) $\mu$m and a peak number density of $3.6(5) \times 10^{10}$ cm$^{
Externí odkaz:
http://arxiv.org/abs/2409.15262