Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Chen Yuncong"'
Autor:
Zhao, Tianxiang, Yu, Wenchao, Wang, Suhang, Wang, Lu, Zhang, Xiang, Chen, Yuncong, Liu, Yanchi, Cheng, Wei, Chen, Haifeng
Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control p
Externí odkaz:
http://arxiv.org/abs/2310.00489
Autor:
Li, Yufei, Liu, Yanchi, Wang, Haoyu, Chen, Zhengzhang, Cheng, Wei, Chen, Yuncong, Yu, Wenchao, Chen, Haifeng, Liu, Cong
Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to detect anomalies in log sequences, they often overlook the significance
Externí odkaz:
http://arxiv.org/abs/2309.05953
Autor:
Zhao, Tianxiang, Yu, Wenchao, Wang, Suhang, Wang, Lu, Zhang, Xiang, Chen, Yuncong, Liu, Yanchi, Cheng, Wei, Chen, Haifeng
Publikováno v:
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), August 6--10, 2023, Long Beach, CA, USA
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, existing algorithms typically require a large number of high-quality demo
Externí odkaz:
http://arxiv.org/abs/2306.07919
Autor:
Luo, Dongsheng, Cheng, Wei, Wang, Yingheng, Xu, Dongkuan, Ni, Jingchao, Yu, Wenchao, Zhang, Xuchao, Liu, Yanchi, Chen, Yuncong, Chen, Haifeng, Zhang, Xiang
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive lear
Externí odkaz:
http://arxiv.org/abs/2303.11911
Publikováno v:
Zhongguo quanke yixue, Vol 27, Iss 07, Pp 834-842 (2024)
Background Since the outbreak of COVID-19 infection, community medical personnel on the front lines of prevention and control of COVID-19 face the risk of direct contact with the virus, as well as problems such as high-intensity work, long working ho
Externí odkaz:
https://doaj.org/article/fca83f9ec66d4a7c9d97af948f0db3c7
Autor:
Zhu, Wei, Song, Dongjin, Chen, Yuncong, Cheng, Wei, Zong, Bo, Mizoguchi, Takehiko, Lumezanu, Cristian, Chen, Haifeng, Luo, Jiebo
Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously distributed among different e
Externí odkaz:
http://arxiv.org/abs/2205.04041
Autor:
Chang, Zhijian, Li, Shumeng, Ye, Jia-Hai, Lin, Fuyan, Chen, Yuncong, Guo, Zijian, He, Weijiang
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 15 January 2025 325
Autor:
Zhao, Xujiang, Zhang, Xuchao, Cheng, Wei, Yu, Wenchao, Chen, Yuncong, Chen, Haifeng, Chen, Feng
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound event detection, which suffers from the over-confidence issue of early-
Externí odkaz:
http://arxiv.org/abs/2202.02441
Autor:
Nazarovs, Jurijs, Lumezanu, Cristian, Ren, Qianying, Chen, Yuncong, Mizoguchi, Takehiko, Song, Dongjin, Chen, Haifeng
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes that are missing during training. This framework relies on two main c
Externí odkaz:
http://arxiv.org/abs/2201.09907
Autor:
Luo, Yefei, Wu, Hao, Liang, Caiyun, Cai, Yanshan, Gu, Yuzhou, Li, Qingmei, Liu, Fanghua, Zhao, Yuteng, Chen, Yuncong, Li, Shunming, Chen, Xi, Jiang, Liyun, Han, Zhigang
Publikováno v:
In Acta Tropica December 2024 260