GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction

By Jianpeng Xu, Pang-Ning Tan, Lifeng Luo, Jiayu Zhou. 2016

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qiang Yang
2. Dacheng Tao
3. Yu Zhang
4. Yu Zheng
5. Xinchao Wang
6. Junbo Zhang
7. Mingsheng Long
8. Yongcheng Jing

Citations in the corpus

• A Survey on Multi-Task Learning (2017, background): “Feature Selection Face and object recognition [211] siRNA efficacy prediction [212] Microblog analysis [119] Behavioral targeting [213] Brain imaging [214] Genetic marker detection [215] Mental state examination [216] Predict cognitive outcome [217] Survival analysis [120] Genetic trait prediction [”
• Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019, background): “Another group of works [16, 23, 28] adopt multi-task learning approaches, which mainly build multiple sub-models for each location and all of these sub-models are trained together by using similarity constraints between locations.”
• Amalgamating Knowledge from Heterogeneous Graph Neural Networks (2021, background): “In the past few years, multi-task learning has been widely studied in various areas, such as bioinformatics [10, 20], ubiquitous computing [39, 40], natural language processing [4, 38] and computer vision [21, 54, 15, 14].”