Kolmogorov-Sinai entropy of an observable bounds its reconstruction error for chaotic dynamics through Lyapunov exponents and Ruelle's inequality.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Four characterizations of irreversibility in training algorithms are equivalent to leading order in step size and produce an emergent force that breaks reparametrization symmetries while favoring minimum entropy production trajectories.
Nonlinear dark-sector interaction models with a half-saturation sparseness scale are observationally preferred over their linear counterparts at >95% confidence for two of three cases.
citing papers explorer
-
Kolmogorov-Sinai entropies identify optimal observables for prediction and dynamics reconstruction in chaotic systems
Kolmogorov-Sinai entropy of an observable bounds its reconstruction error for chaotic dynamics through Lyapunov exponents and Ruelle's inequality.
-
Thermodynamic Irreversibility of Training Algorithms
Four characterizations of irreversibility in training algorithms are equivalent to leading order in step size and produce an emergent force that breaks reparametrization symmetries while favoring minimum entropy production trajectories.
-
Saturation Mechanisms in the Interacting Dark Sector
Nonlinear dark-sector interaction models with a half-saturation sparseness scale are observationally preferred over their linear counterparts at >95% confidence for two of three cases.