Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Ma, Lintao"'
The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy mas
Externí odkaz:
http://arxiv.org/abs/2408.01000
Point tracking is a challenging task in computer vision, aiming to establish point-wise correspondence across long video sequences. Recent advancements have primarily focused on temporal modeling techniques to improve local feature similarity, often
Externí odkaz:
http://arxiv.org/abs/2407.20730
Autor:
Wang, Shiyu, Chu, Zhixuan, Sun, Yinbo, Liu, Yu, Guo, Yuliang, Chen, Yang, Jian, Huiyang, Ma, Lintao, Lu, Xingyu, Zhou, Jun
Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with transformer-based forecasting models, challenges remain due to the non
Externí odkaz:
http://arxiv.org/abs/2407.19697
Autor:
Zhou, Fan, Pan, Chen, Ma, Lintao, Liu, Yu, Zhang, James, Zhou, Jun, Mei, Hongyuan, Lin, Weitao, Zhuang, Zi, Ning, Wenxin, Hu, Yunhua, Xue, Siqiao
Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coh
Externí odkaz:
http://arxiv.org/abs/2406.12242
Autor:
Wang, Shiyu, Wu, Haixu, Shi, Xiaoming, Hu, Tengge, Luo, Huakun, Ma, Lintao, Zhang, James Y., Zhou, Jun
Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate temporal variations, making forecasting extremely challenging. Going beyond
Externí odkaz:
http://arxiv.org/abs/2405.14616
Autor:
Yang, Yiyuan, Jin, Ming, Wen, Haomin, Zhang, Chaoli, Liang, Yuxuan, Ma, Lintao, Wang, Yi, Liu, Chenghao, Yang, Bin, Xu, Zenglin, Bian, Jiang, Pan, Shirui, Wen, Qingsong
The study of time series is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a d
Externí odkaz:
http://arxiv.org/abs/2404.18886
The recent boom of linear forecasting models questions the ongoing passion for architectural modifications of Transformer-based forecasters. These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series,
Externí odkaz:
http://arxiv.org/abs/2310.06625
Autor:
Lin, Yong, Zhou, Fan, Tan, Lu, Ma, Lintao, Liu, Jiameng, He, Yansu, Yuan, Yuan, Liu, Yu, Zhang, James, Yang, Yujiu, Wang, Hao
Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continuous domains, current invariance learning techniques generally assume c
Externí odkaz:
http://arxiv.org/abs/2310.05348
Autor:
Jin, Ming, Wang, Shiyu, Ma, Lintao, Chu, Zhixuan, Zhang, James Y., Shi, Xiaoming, Chen, Pin-Yu, Liang, Yuxuan, Li, Yuan-Fang, Pan, Shirui, Wen, Qingsong
Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for
Externí odkaz:
http://arxiv.org/abs/2310.01728
Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames. Recent MOT approaches can be categorized into two-stage tracking-by-detection (TBD) metho
Externí odkaz:
http://arxiv.org/abs/2308.09905