1 code implementation • 25 Oct 2023 • Jiaxu Cui, Bingyi Sun, Jiming Liu, Bo Yang
Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains.
no code implementations • 6 Aug 2023 • Mutong Liu, Yang Liu, Jiming Liu
Next, we describe the development and components of various machine learning models for infectious disease risk prediction.
no code implementations • 19 Apr 2023 • Jinfu Ren, Yang Liu, Jiming Liu
Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains.
no code implementations • 14 Sep 2020 • Qi Tan, Yang Liu, Jiming Liu
Deep learning has achieved incredible success over the past years, especially in various challenging predictive spatio-temporal analytics (PSTA) tasks, such as disease prediction, climate forecast, and traffic prediction, where intrinsic dependency relationships among data exist and generally manifest at multiple spatio-temporal scales.
no code implementations • ECCV 2020 • Yuexin Ma, Xinge ZHU, Xinjing Cheng, Ruigang Yang, Jiming Liu, Dinesh Manocha
Then we aggregate dynamic points to instance points, which stand for moving objects such as pedestrians in videos.
no code implementations • 22 Aug 2019 • Chao Yu, Jiming Liu, Shamim Nemati
As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback.
no code implementations • 3 Aug 2018 • Xiao-Feng Xie, Jiming Liu, Zun-Jing Wang
A cooperative group optimization (CGO) system is presented to implement CGO cases by integrating the advantages of the cooperative group and low-level algorithm portfolio design.
no code implementations • 21 Nov 2017 • Hongbin Pei, Bo Yang, Jiming Liu, Lei Dong
To address the challenge, we study the problem of active surveillance, i. e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels.