no code implementations • 19 Jun 2023 • Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, Yanbin Lu
The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results.
no code implementations • 4 Sep 2020 • Xinli Yu, Mohsen Malmir, Cynthia He, Yue Liu, Rex Wu
However, the inference time will not be a problem for our model since our model has a simple architecture which enables efficient training and inference.
no code implementations • 16 May 2020 • Zheng Chen, Xinli Yu, Yuan Ling, Xiaohua Hu
Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes.
no code implementations • 14 Jan 2019 • Xinli Yu, Zheng Chen, Wei-Shih Yang, Xiaohua Hu, Erjia Yan
This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI).
no code implementations • 19 Dec 2018 • Zheng Chen, Xinli Yu, Chi Zhang, Jin Zhang, Cui Lin, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang, Erjia Yan
Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc.
no code implementations • 19 Dec 2018 • Zheng Chen, Xinli Yu, Yuan Ling, Bo Song, Wei Quan, Xiaohua Hu, Erjia Yan
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.