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Found 7 papers, 1 papers with code
Universality of Winning Tickets: A Renormalization Group Perspective
Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.
Universality of Deep Neural Network Lottery Tickets: A Renormalization Group Perspective
Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.
Local error quantification for Neural Network Differential Equation solvers
Neural networks have been identified as powerful tools for the study of complex systems.
Error Estimation and Correction from within Neural Network Differential Equation Solvers
Our methods do not require advance knowledge of the true solutions and obtain explicit relationships between loss functions and the error associated with solution estimates.
Optimizing Neural Networks via Koopman Operator Theory
Koopman operator theory, a powerful framework for discovering the underlying dynamics of nonlinear dynamical systems, was recently shown to be intimately connected with neural network training.
Dynamical Systems and Neural Networks
Neural Networks (NNs) have been identified as a potentially powerful tool in the study of complex dynamical systems.
Dynamical Systems Signal Processing
Hamiltonian neural networks for solving equations of motion
There has been a wave of interest in applying machine learning to study dynamical systems.
