254 citations · 981 across the 13 of their papers we have counts for
13 papers
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…
Differentially Private Label Protection in Split Learning
Xin Yang, Jiankai Sun, Yuanshun Yao +2
Split learning is a distributed training framework that allows multiple parties to jointly train a machine learning model over vertically partitioned data (partitioned by attribute…
Defending against Reconstruction Attack in Vertical Federated Learning
Jiankai Sun, Yuanshun Yao, Weihao Gao +2
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared…
Vertical Federated Learning without Revealing Intersection Membership
Jiankai Sun, Xin Yang, Yuanshun Yao +4
Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model.…
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang +5
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the pri…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
Bag of Freebies for Training Object Detection Neural Networks
Zhi Zhang, Tong He, Hang Zhang +3
Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}. Object detection models, however, have more complex neural network structures an…
Bag of Tricks for Image Classification with Convolutional Neural Networks
Tong He, Zhi Zhang, Hang Zhang +3
Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.…
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie +3
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all at…
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
Junyuan Xie, Ross Girshick, Ali Farhadi
As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In…
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has fo…
A Social Influence Model Based On Circuit Theory
Biao Xiang, Enhong Chen, Qi Liu +3
Understanding the behaviors of information propagation is essential for the effective exploitation of social influence in social networks. However, few existing influence models ar…