Zobrazeno 1 - 10
of 889
pro vyhledávání: '"Yeh, Chin An"'
Autor:
Yeh, Chin-Chia Michael, Der, Audrey, Saini, Uday Singh, Lai, Vivian, Zheng, Yan, Wang, Junpeng, Dai, Xin, Zhuang, Zhongfang, Fan, Yujie, Chen, Huiyuan, Aboagye, Prince Osei, Wang, Liang, Zhang, Wei, Keogh, Eamonn
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in r
Externí odkaz:
http://arxiv.org/abs/2409.09298
Autor:
Wang, Liang, Jain, Shubham, Dou, Yingtong, Wang, Junpeng, Yeh, Chin-Chia Michael, Fan, Yujie, Aboagye, Prince, Zheng, Yan, Dai, Xin, Zhuang, Zhongfang, Saini, Uday Singh, Zhang, Wei
Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score remains largely unexplored. Existing research often relies on narrowly
Externí odkaz:
http://arxiv.org/abs/2409.04649
Autor:
Der, Audrey, Yeh, Chin-Chia Michael, Dai, Xin, Chen, Huiyuan, Zheng, Yan, Fan, Yujie, Zhuang, Zhongfang, Lai, Vivian, Wang, Junpeng, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-s
Externí odkaz:
http://arxiv.org/abs/2408.07869
Principal component analysis (PCA) is a widely used dimension reduction method, but its performance is known to be non-robust to outliers. Recently, product-PCA (PPCA) has been shown to possess the efficiency-loss free ordering-robustness property: (
Externí odkaz:
http://arxiv.org/abs/2407.19725
Convolutional Neural Networks (CNNs) have dominated the majority of computer vision tasks. However, CNNs' vulnerability to adversarial attacks has raised concerns about deploying these models to safety-critical applications. In contrast, the Human Vi
Externí odkaz:
http://arxiv.org/abs/2405.06345
Autor:
Chen, Huiyuan, Xu, Zhe, Yeh, Chin-Chia Michael, Lai, Vivian, Zheng, Yan, Xu, Minghua, Tong, Hanghang
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, the quadratic space and time complexity hinders the scalability of Grap
Externí odkaz:
http://arxiv.org/abs/2405.04028
As deep generative models advance, we anticipate deepfakes achieving "perfection"-generating no discernible artifacts or noise. However, current deepfake detectors, intentionally or inadvertently, rely on such artifacts for detection, as they are exc
Externí odkaz:
http://arxiv.org/abs/2405.00483
Autor:
Yeh, Chin-Chia Michael, Fan, Yujie, Dai, Xin, Saini, Uday Singh, Lai, Vivian, Aboagye, Prince Osei, Wang, Junpeng, Chen, Huiyuan, Zheng, Yan, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei
Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal forecasting problems using general time series forecasting models, i.e.,
Externí odkaz:
http://arxiv.org/abs/2402.10487
Autor:
Yeh, Chin-Chia Michael, Chen, Huiyuan, Fan, Yujie, Dai, Xin, Zheng, Yan, Lai, Vivian, Wang, Junpeng, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foreground subsequences have already been extracted, with each subsequence corr
Externí odkaz:
http://arxiv.org/abs/2311.02561
Autor:
Der, Audrey, Yeh, Chin-Chia Michael, Zheng, Yan, Wang, Junpeng, Chen, Huiyuan, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to privacy regulations or fear of leaking confidential business informat
Externí odkaz:
http://arxiv.org/abs/2311.02563