Transfer Meta-Learning for State Space Autoencoders

Nonlinear system identification often requires costly experimental campaigns and separate model fitting for each different instance (target system) of a given system family. To reduce this burden, meta-learning approaches reuse information acquired across related systems. In particular, in-context m