9 code implementations • 12 Feb 2018 • Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, Honglin Qiao
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
1 code implementation • 30 Nov 2020 • Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Bin Dong, Xinyu Zhou
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on Out-of-Distribution Detection on MS-1M vs. IJB-C
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 5 Feb 2024 • Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, QIngwei Lin, Haiming Zhang, Jianhui Li, Gaogang Xie
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
1 code implementation • 17 Aug 2023 • Haotian Si, Changhua Pei, Zhihan Li, Yadong Zhao, Jingjing Li, Haiming Zhang, Zulong Diao, Jianhui Li, Gaogang Xie, Dan Pei
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
no code implementations • 31 May 2019 • Haowen Xu, Wenxiao Chen, Jinlin Lai, Zhihan Li, Youjian Zhao, Dan Pei
Using powerful posterior distributions is a popular approach to achieving better variational inference.
no code implementations • 25 Sep 2019 • Haowen Xu, Wenxiao Chen, Jinlin Lai, Zhihan Li, Youjian Zhao, Dan Pei
Using powerful posterior distributions is a popular technique in variational inference.
no code implementations • 6 Mar 2023 • Bixing Yan, Shaoling Chen, Yuxuan He, Zhihan Li
Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data.