31 citations · 69 across the 8 of their papers we have counts for
11 papers · 1 filter
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…
The Role of Isomorphism Classes in Multi-Relational Datasets
Vijja Wichitwechkarn, Ben Day, Cristian Bodnar +2
Multi-interaction systems abound in nature, from colloidal suspensions to gene regulatory circuits. These systems can produce complex dynamics and graph neural networks have been p…
Message Passing Neural Processes
Ben Day, Cătălina Cangea, Arian R. Jamasb +1
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity. However,…
Uncertainty in Neural Relational Inference Trajectory Reconstruction
Vasileios Karavias, Ben Day, Pietro Liò
Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and unde…
On Second Order Behaviour in Augmented Neural ODEs
Alexander Norcliffe, Cristian Bodnar, Ben Day +2
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures. The continuous nature of NODEs has m…