Robust Dynamic Trajectory Regression on Road Networks: A Multi-task Learning Framework

By Aiqing Huang, Linli Xu, Yitan Li, Enhong Chen. 2014

8 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Graph Learning, Signal Processing

How researchers see this paper

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

In Artificial Intelligence

1. Qiang Yang
2. Yu Zhang
3. Mingsheng Long
4. Jianmin Wang
5. Dongrui Wu
6. Sinno Jialin Pan
7. Sebastian Ruder
8. Philip S. Yu

In Building and Construction

1. Qiang Yang
2. Yu Zhang

In Graph Learning

1. Qiang Yang
2. Yu Zhang

In Signal Processing

1. Qiang Yang
2. Yu Zhang
3. Yu Zheng
4. Mingsheng Long
5. Jianmin Wang
6. Sinno Jialin Pan

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “Applications of MTL in ubiquitous computing include stock prediction [126], multi-device localization [127], the inverse dynamics problem for robotics [128,129], estimation of travel costs on road networks [130], travel-time prediction on road networks [131], and traffic-sign recognition [132].”
• A Survey on Multi-Task Learning (2017, methodology): “In [212], a multi-task decomposition trajectory regression method, which captures the spatial and temporal information”