34 citations · 37 across the 4 of their papers we have counts for
4 papers · 1 filter
Modular Neural Ordinary Differential Equations
Max Zhu, Pietro Lio, Jacob Moss
The laws of physics have been written in the language of dif-ferential equations for centuries. Neural Ordinary Differen-tial Equations (NODEs) are a new machine learning architect…
Approximate Latent Force Model Inference
Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu +1
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems. They carry the structure of differential…
Meta-learning using privileged information for dynamics
Ben Day, Alexander Norcliffe, Jacob Moss +1
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information. This flexibil…
Neural ODE Processes
Alexander Norcliffe, Cristian Bodnar, Ben Day +2
Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability…