Zobrazeno 1 - 10
of 4 936
pro vyhledávání: '"Jiang, Xi"'
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:
Kang, Yanqing, Zhu, Di, Zhang, Haiyang, Shi, Enze, Yu, Sigang, Wu, Jinru, Wang, Xuhui, Liu, Xuan, Chen, Geng, Jiang, Xi, Zhang, Tuo, Zhang, Shu
Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes. However, this approach relies heavily on prior knowledge from graph theo
Externí odkaz:
http://arxiv.org/abs/2409.11174
Autor:
Wang, Jiaqi, Jiang, Hanqi, Liu, Yiheng, Ma, Chong, Zhang, Xu, Pan, Yi, Liu, Mengyuan, Gu, Peiran, Xia, Sichen, Li, Wenjun, Zhang, Yutong, Wu, Zihao, Liu, Zhengliang, Zhong, Tianyang, Ge, Bao, Zhang, Tuo, Qiang, Ning, Hu, Xintao, Jiang, Xi, Zhang, Xin, Zhang, Wei, Shen, Dinggang, Liu, Tianming, Zhang, Shu
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-inclu
Externí odkaz:
http://arxiv.org/abs/2408.01319
Autor:
Zhang, Yutong, Pan, Yi, Zhong, Tianyang, Dong, Peixin, Xie, Kangni, Liu, Yuxiao, Jiang, Hanqi, Liu, Zhengliang, Zhao, Shijie, Zhang, Tuo, Jiang, Xi, Shen, Dinggang, Liu, Tianming, Zhang, Xin
Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinical decision-making. However, the diversity and cross-source heterogeneity of these data challenge the g
Externí odkaz:
http://arxiv.org/abs/2407.05758
Autor:
Chu, Andrew, Jiang, Xi, Liu, Shinan, Bhagoji, Arjun, Bronzino, Francesco, Schmitt, Paul, Feamster, Nick
Many problems in computer networking rely on parsing collections of network traces (e.g., traffic prioritization, intrusion detection). Unfortunately, the availability and utility of these collections is limited due to privacy concerns, data stalenes
Externí odkaz:
http://arxiv.org/abs/2406.02784
Autor:
Ge, Yanqi, Liu, Jiaqi, Fan, Qingnan, Jiang, Xi, Huang, Ye, Qin, Shuai, Gu, Hong, Li, Wen, Duan, Lixin
In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I) diffusion models. The main challenge is consistent structure preservation while enabling effective style transfer effects. The past approaches in thi
Externí odkaz:
http://arxiv.org/abs/2404.06835
Autor:
Jiang, Xi, Chen, Ying, Nie, Qiang, Liu, Yong, Liu, Jianlin, Gao, Bin-Bin, Liu, Jun, Wang, Chengjie, Zheng, Feng
Publikováno v:
Advances in Neural Information Processing Systems 35, ISBN: 9781713871088, (2022)
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an i
Externí odkaz:
http://arxiv.org/abs/2403.14233
In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they of
Externí odkaz:
http://arxiv.org/abs/2403.14213
Prediction the conversion to early-stage dementia is critical for mitigating its progression but remains challenging due to subtle cognitive impairments and structural brain changes. Traditional T1-weighted magnetic resonance imaging (T1-MRI) researc
Externí odkaz:
http://arxiv.org/abs/2403.13338
Autor:
Li, Pengzhi, Nie, Qiang, Chen, Ying, Jiang, Xi, Wu, Kai, Lin, Yuhuan, Liu, Yong, Peng, Jinlong, Wang, Chengjie, Zheng, Feng
Publikováno v:
ECCV 2024
Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regenerating the entire image; 2) guidance solely by a reference image or te
Externí odkaz:
http://arxiv.org/abs/2403.12658