Zobrazeno 1 - 10
of 348
pro vyhledávání: '"Liang, Yuxuan"'
Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In
Externí odkaz:
http://arxiv.org/abs/2411.09251
Human trajectory modeling is essential for deciphering movement patterns and supporting advanced applications across various domains. However, existing methods are often tailored to specific tasks and regions, resulting in limitations related to task
Externí odkaz:
http://arxiv.org/abs/2411.03859
Autor:
Tian, Jindong, Liang, Yuxuan, Xu, Ronghui, Chen, Peng, Guo, Chenjuan, Zhou, Aoying, Pan, Lujia, Rao, Zhongwen, Yang, Bin
Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-based model
Externí odkaz:
http://arxiv.org/abs/2410.19892
Autor:
Zhang, Guibin, Dong, Haonan, Zhang, Yuchen, Li, Zhixun, Chen, Dingshuo, Wang, Kai, Chen, Tianlong, Liang, Yuxuan, Cheng, Dawei, Wang, Kun
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset selection have been developed to streamline data volume by retaining, s
Externí odkaz:
http://arxiv.org/abs/2410.13761
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling
Externí odkaz:
http://arxiv.org/abs/2410.12360
Autor:
Chen, Wei, Liang, Yuxuan
The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks have achieved success in modeling va
Externí odkaz:
http://arxiv.org/abs/2410.12593
Autor:
Liu, Xu, Liu, Juncheng, Woo, Gerald, Aksu, Taha, Liang, Yuxuan, Zimmermann, Roger, Liu, Chenghao, Savarese, Silvio, Xiong, Caiming, Sahoo, Doyen
Time series foundation models have demonstrated impressive performance as zero-shot forecasters. However, achieving effectively unified training on time series remains an open challenge. Existing approaches introduce some level of model specializatio
Externí odkaz:
http://arxiv.org/abs/2410.10469
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. According to the embedding theory, dynamical systems and time series can be m
Externí odkaz:
http://arxiv.org/abs/2410.06651
Multimodal imaging has shown great potential in cancer research by concurrently providing anatomical, functional, and molecular information in live, intact animals. During preclinical imaging of small animals like mice, anesthesia is required to prev
Externí odkaz:
http://arxiv.org/abs/2410.07517
Autor:
Yu, Miao, Mao, Junyuan, Zhang, Guibin, Ye, Jingheng, Fang, Junfeng, Zhong, Aoxiao, Liu, Yang, Liang, Yuxuan, Wang, Kun, Wen, Qingsong
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cogn
Externí odkaz:
http://arxiv.org/abs/2410.01677