Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Kieu, Tung"'
The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and enabling impro
Externí odkaz:
http://arxiv.org/abs/2407.06881
We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models on edge devices near sensors such that decisions can
Externí odkaz:
http://arxiv.org/abs/2404.13990
Text-video retrieval, a prominent sub-field within the domain of multimodal information retrieval, has witnessed remarkable growth in recent years. However, existing methods assume video scenes are consistent with unbiased descriptions. These limitat
Externí odkaz:
http://arxiv.org/abs/2312.09507
Publikováno v:
Proceedings of the ACM on Management of Data 1, 2 (2023), 171:1-171:27
Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy i
Externí odkaz:
http://arxiv.org/abs/2302.12721
Autor:
Zhao, Yan, Deng, Liwei, Chen, Xuanhao, Guo, Chenjuan, Yang, Bin, Kieu, Tung, Huang, Feiteng, Pedersen, Torben Bach, Zheng, Kai, Jensen, Christian S.
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable rel
Externí odkaz:
http://arxiv.org/abs/2209.04635
A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. While recent attention-based forecasting models show strong abilities i
Externí odkaz:
http://arxiv.org/abs/2204.13767
Autor:
Kieu, Tung, Yang, Bin, Guo, Chenjuan, Jensen, Christian S., Zhao, Yan, Huang, Feiteng, Zheng, Kai
Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerab
Externí odkaz:
http://arxiv.org/abs/2204.03341
Traffic time series forecasting is challenging due to complex spatio-temporal dynamics time series from different locations often have distinct patterns; and for the same time series, patterns may vary across time, where, for example, there exist cer
Externí odkaz:
http://arxiv.org/abs/2203.15737
Publikováno v:
In Expert Systems With Applications 15 September 2024 250
Autor:
Campos, David, Kieu, Tung, Guo, Chenjuan, Huang, Feiteng, Zheng, Kai, Yang, Bin, Jensen, Christian S.
Publikováno v:
Proceedings of the VLDB Endowment, 15, 3 (2022), 611-623
With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this
Externí odkaz:
http://arxiv.org/abs/2111.11108