Out-of-Domain Generalization in Dynamical Systems Reconstruction In this work, we provide a formal framework that addresses generalization in DSR We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning
Out-of-Domain Generalization in Dynamical Systems Reconstruction - PMLR We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems Reconstruction In this work, we provide a formal framework that addresses generalization in DSR We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning
Out-of-Domain Generalization in Dynamical Systems Reconstruction The paper delineates a profound inquiry into the capabilities and constraints of current data-driven approaches for reconstructing dynamical systems (DS) from time-series data, particularly focusing on out-of-domain generalization (OODG)
Out-of-Domain Generalization in Dynamical Systems . . . We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems Reconstruction We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
DFG - GEPRIS - Out-of-Domain Generalization in Dynamical Systems . . . In WP 3 we will test the most promising algorithms for OOD generalization on neurophysiological and behavioral data, testing generalization to behavioral rules and neural activity not provided for model training, and generating novel predictions for further behavioral and optogenetic experiments