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Found 8 papers, 1 papers with code
Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes
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.
Machine Learning for Infectious Disease Risk Prediction: A Survey
Next, we describe the development and components of various machine learning models for infectious disease risk prediction.
Decadal Temperature Prediction via Chaotic Behavior Tracking
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.
Demystifying Deep Learning in Predictive Spatio-Temporal Analytics: An Information-Theoretic Framework
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.
AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points
Then we aggregate dynamic points to instance points, which stand for moving objects such as pedestrians in videos.
Reinforcement Learning in Healthcare: A Survey
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.
A Cooperative Group Optimization System
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.
Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics
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.
