Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Xu, Tingyang"'
Autor:
Han, Jiaqi, Cen, Jiacheng, Wu, Liming, Li, Zongzhao, Kong, Xiangzhe, Jiao, Rui, Yu, Ziyang, Xu, Tingyang, Wu, Fandi, Wang, Zihe, Xu, Hongteng, Wei, Zhewei, Liu, Yang, Rong, Yu, Huang, Wenbing
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections, making them i
Externí odkaz:
http://arxiv.org/abs/2403.00485
Autor:
Wang, Haoyu, Ma, Guozheng, Meng, Ziqiao, Qin, Zeyu, Shen, Li, Zhang, Zhong, Wu, Bingzhe, Liu, Liu, Bian, Yatao, Xu, Tingyang, Wang, Xueqian, Zhao, Peilin
Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and training steps in a single round, which may overlook the continuously
Externí odkaz:
http://arxiv.org/abs/2402.07610
Autor:
Wang, Qichao, Bian, Tian, Yin, Yian, Xu, Tingyang, Cheng, Hong, Meng, Helen M., Zheng, Zibin, Chen, Liang, Wu, Bingzhe
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining increasing re
Externí odkaz:
http://arxiv.org/abs/2310.11778
Autor:
Liu, Yang, Cheng, Jiashun, Zhao, Haihong, Xu, Tingyang, Zhao, Peilin, Tsung, Fugee, Li, Jia, Rong, Yu
Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of physical induc
Externí odkaz:
http://arxiv.org/abs/2308.13212
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network outp
Externí odkaz:
http://arxiv.org/abs/2306.12091
Autor:
Zhang, Hengtong, Xu, Tingyang
By formulating data samples' formation as a Markov denoising process, diffusion models achieve state-of-the-art performances in a collection of tasks. Recently, many variants of diffusion models have been proposed to enable controlled sample generati
Externí odkaz:
http://arxiv.org/abs/2304.07132
Autor:
Bian, Tian, Jiang, Yuli, Li, Jia, Xu, Tingyang, Rong, Yu, Su, Yi, Kwok, Timothy, Meng, Helen, Cheng, Hong
Publikováno v:
ICDE2023
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This
Externí odkaz:
http://arxiv.org/abs/2303.02405
Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobil
Externí odkaz:
http://arxiv.org/abs/2212.05707
Autor:
Gao, Ziqi, Niu, Yifan, Cheng, Jiashun, Tang, Jianheng, Xu, Tingyang, Zhao, Peilin, Li, Lanqing, Tsung, Fugee, Li, Jia
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupl
Externí odkaz:
http://arxiv.org/abs/2211.16771
The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing. In this p
Externí odkaz:
http://arxiv.org/abs/2209.11577