3 papers
cs.LG2020
LINDT: Tackling Negative Federated Learning with Local Adaptation
Hong Lin, Lidan Shou, Ke Chen +2
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without dis…
cs.DB2020
An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing
Tiantian Liu, Huan Li, Hua Lu +2
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries. To support such queries and indoor LBS, multiple tec…
cs.CL2019
Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction
Jue Wang, Ke Chen, Lidan Shou +2
This paper addresses the problem of key phrase extraction from sentences. Existing state-of-the-art supervised methods require large amounts of annotated data to achieve good perfo…