1 code implementation • 12 Apr 2022 • Chunxu Tang, Beinan Wang, Zhenxiao Luo, Huijun Wu, Shajan Dasan, Maosong Fu, Yao Li, Mainak Ghosh, Ruchin Kabra, Nikhil Kantibhai Navadiya, Da Cheng, Fred Dai, Vrushali Channapattan, Prachi Mishra
We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine.
no code implementations • 12 Sep 2021 • Xugang Wu, Huijun Wu, Xu Zhou, Kai Lu
Graph data, in most cases, has two views of information, namely structure information and feature information.
2 code implementations • 5 Mar 2019 • Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu
Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.
no code implementations • 12 Sep 2017 • Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu
In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model.