Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Sun QingYun"'
Publikováno v:
Cailiao Baohu, Vol 57, Iss 4, Pp 11-19 (2024)
For guiding the growth regulation of high-purity TaC coatings for semiconductor high-temperature furnace components, the high-throughput thermodynamic simulation calculation of TaC coating growth process was carried out by Thermo-calc software, and t
Externí odkaz:
https://doaj.org/article/41ce2fe8530b459595b2b71c6d3d56da
Autor:
Yuan, Haonan, Sun, Qingyun, Wang, Zhaonan, Fu, Xingcheng, Ji, Cheng, Wang, Yongjian, Jin, Bo, Li, Jianxin
Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynami
Externí odkaz:
http://arxiv.org/abs/2412.08160
We propose a scaling law hypothesis for multimodal models processing text, audio, images, and video within a shared token and embedding space. Our framework predicts model performance based on modality-specific compression and tokenization efficiency
Externí odkaz:
http://arxiv.org/abs/2409.06754
Autor:
Sun, Qingyun, Chen, Ziying, Yang, Beining, Ji, Cheng, Fu, Xingcheng, Zhou, Sheng, Peng, Hao, Li, Jianxin, Yu, Philip S.
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retain
Externí odkaz:
http://arxiv.org/abs/2407.00615
Reinforcement learning (RL) faces challenges in evaluating policy trajectories within intricate game tasks due to the difficulty in designing comprehensive and precise reward functions. This inherent difficulty curtails the broader application of RL
Externí odkaz:
http://arxiv.org/abs/2406.19644
Autor:
Qin, Jiawen, Yuan, Haonan, Sun, Qingyun, Xu, Lyujin, Yuan, Jiaqi, Huang, Pengfeng, Wang, Zhaonan, Fu, Xingcheng, Peng, Hao, Li, Jianxin, Yu, Philip S.
Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data distributions, where certain parts
Externí odkaz:
http://arxiv.org/abs/2406.09870
Autor:
Zhou, Zhiyao, Zhou, Sheng, Mao, Bochao, Chen, Jiawei, Sun, Qingyun, Feng, Yan, Chen, Chun, Wang, Can
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numerous GSL methods, the progresse
Externí odkaz:
http://arxiv.org/abs/2406.08897
Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computat
Externí odkaz:
http://arxiv.org/abs/2405.03188
Autor:
Lu, Feihong, Wang, Weiqi, Luo, Yangyifei, Zhu, Ziqin, Sun, Qingyun, Xu, Baixuan, Shi, Haochen, Gao, Shiqi, Li, Qian, Song, Yangqiu, Li, Jianxin
Social media has become a ubiquitous tool for connecting with others, staying updated with news, expressing opinions, and finding entertainment. However, understanding the intention behind social media posts remains challenging due to the implicitnes
Externí odkaz:
http://arxiv.org/abs/2402.18169
Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive predictive abilities by exploiting th
Externí odkaz:
http://arxiv.org/abs/2402.06716