Search Results for author:
Found 3 papers, 2 papers with code
Interpretable Meta-Learning of Physical Systems
Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions.
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Model-based reinforcement learning is a powerful tool, but collecting data to fit an accurate model of the system can be costly.
Online greedy identification of linear dynamical systems
This work addresses the problem of exploration in an unknown environment.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
