machinelearning-dynamic.github.io - When Machine Learning meets Dynamical Systems: Theory and Applications

Example domain paragraphs

Workshop at AAAI 2023 - February 13, 2023

Machine learning (ML) models have gained much attention for solving static problems such as computer vision thanks to their efficiency and generalization ability in extracting knowledge and patterns from stationary objects. However, the world is constantly changing: emerging challenges for artificial intelligence lie in the realm of dynamical systems, where it is crucial to absorb new knowledge and learn temporal evolutions. With the flexibility to capture the world's dynamics, ML models usually achieve sta

With this forethought, we are opening this workshop to call for deeper and more specialized contributions from researchers on these dynamic system applications for machine learning, which include (but are not limited to): (1) Optimization Algorithms for Learning Dynamical Systems; (2) Special ML structures for Learning Dynamical Systems; (3) Trustworthiness of ML-based Dynamical Systems; (4) Practical Applications of Data-driven Modeling; (5) Temporal Feature Analysis for Time Series Data; (6) Dynamical Sys

Links to machinelearning-dynamic.github.io (4)

thaipduong.github.io Thai Duong
lamnguyen-mltd.github.io Lam M. Nguyen
nguyenvchuong.github.io Chuong Nguyen
lamnguyen.org Lam M. Nguyen