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Found 6 papers, 3 papers with code
Contractive Systems Improve Graph Neural Networks Against Adversarial Attacks
Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks.
Designing Stable Neural Networks using Convex Analysis and ODEs
Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are appropriately constrained.
Predictions Based on Pixel Data: Insights from PDEs and Finite Differences
Neural networks are the state-of-the-art for many approximation tasks in high-dimensional spaces, as supported by an abundance of experimental evidence.
Dynamical systems' based neural networks
The structure of the neural network is then inferred from the properties of the ODE vector field.
Learning Hamiltonians of constrained mechanical systems
Recently, there has been an increasing interest in modelling and computation of physical systems with neural networks.
Lie Group integrators for mechanical systems
Finally, we show how Lie group integrators can be applied to model the controlled path of a payload being transported by two rotors.
Image Registration
Numerical Analysis
Numerical Analysis
Dynamical Systems
65L05, 70E55
