Zobrazeno 1 - 10
of 267
pro vyhledávání: '"Yuan, Xinyu"'
The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ab
Externí odkaz:
http://arxiv.org/abs/2410.15716
Autor:
Yuan, Xinyu, Zhan, Zhihao, Zhang, Zuobai, Zhou, Manqi, Zhao, Jianan, Han, Boyu, Li, Yue, Tang, Jian
Transcriptome foundation models TFMs hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex
Externí odkaz:
http://arxiv.org/abs/2408.12373
Autor:
Yuan, Xinyu, Qiao, Yan
Denoising diffusion probabilistic models (DDPMs) are becoming the leading paradigm for generative models. It has recently shown breakthroughs in audio synthesis, time series imputation and forecasting. In this paper, we propose Diffusion-TS, a novel
Externí odkaz:
http://arxiv.org/abs/2403.01742
Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language. Knowledge graphs (KGs) have different entity and relati
Externí odkaz:
http://arxiv.org/abs/2310.04562
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to explicitly acquire protein functions, which is the end goal of protein re
Externí odkaz:
http://arxiv.org/abs/2301.12040
Autor:
Zhu, Zhaocheng, Yuan, Xinyu, Galkin, Mikhail, Xhonneux, Sophie, Zhang, Ming, Gazeau, Maxime, Tang, Jian
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*N
Externí odkaz:
http://arxiv.org/abs/2206.04798
Package theft detection has been a challenging task mainly due to lack of training data and a wide variety of package theft cases in reality. In this paper, we propose a new Global and Local Fusion Package Theft Detection Embedding (GLF-PTDE) framewo
Externí odkaz:
http://arxiv.org/abs/2205.11804
Publikováno v:
In Separation and Purification Technology 6 September 2024 343
Autor:
Zhu, Zhaocheng, Shi, Chence, Zhang, Zuobai, Liu, Shengchao, Xu, Minghao, Yuan, Xinyu, Zhang, Yangtian, Chen, Junkun, Cai, Huiyu, Lu, Jiarui, Ma, Chang, Liu, Runcheng, Xhonneux, Louis-Pascal, Qu, Meng, Tang, Jian
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks and data preprocessing pipe
Externí odkaz:
http://arxiv.org/abs/2202.08320
Autor:
Hsu, Hung-Min1 (AUTHOR) hmhsu@uw.edu, Yuan, Xinyu1 (AUTHOR), Chuang, Yun-Yen2 (AUTHOR) d09921007@ntu.edu.tw, Sun, Wei3 (AUTHOR) wsun91@uw.edu, Chang, Ray-I4 (AUTHOR) rayichang@ntu.edu.tw
Publikováno v:
Sensors (14248220). Aug2024, Vol. 24 Issue 15, p4773. 15p.