7 citations · 19 across the 6 of their papers we have counts for
6 papers
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
Jie Zhu, Leye Wang, Xiao Han
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smar…
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Xiao Han, Leye Wang, Junjie Wu +1
Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private informati…
Label Confusion Learning to Enhance Text Classification Models
Biyang Guo, Songqiao Han, Xiao Han +2
Representing a true label as a one-hot vector is a common practice in training text classification models. However, the one-hot representation may not adequately reflect the relati…
Federated Crowdsensing: Framework and Challenges
Leye Wang, Han Yu, Xiao Han
Crowdsensing is a promising sensing paradigm for smart city applications (e.g., traffic and environment monitoring) with the prevalence of smart mobile devices and advanced network…
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Xiao Han, Ruiqing Ding, Leye Wang +1
Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable infor…
Geographic Differential Privacy for Mobile Crowd Coverage Maximization
Leye Wang, Gehua Qin, Dingqi Yang +2
For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locatio…