364 citations · 439 across the 5 of their papers we have counts for
9 papers
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…
Secret Sharing based Secure Regressions with Applications
Chaochao Chen, Liang Li, Wenjing Fang +6
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potent…
AGL: a Scalable System for Industrial-purpose Graph Machine Learning
Dalong Zhang, Xin Huang, Ziqi Liu +8
Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as…
Industrial Scale Privacy Preserving Deep Neural Network
Longfei Zheng, Chaochao Chen, Yingting Liu +6
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become…
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
Cen Chen, Chen Liang, Jianbin Lin +7
The insurance industry has been creating innovative products around the emerging online shopping activities. Such e-commerce insurance is designed to protect buyers from potential…
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
Yankun Ren, Jianbin Lin, Siliang Tang +4
Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods ge…