1 citations · 2 across the 4 of their papers we have counts for
7 papers · 1 filter
Subgraph Concept Networks: Concept Levels in Graph Classification
Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1
The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based…
Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe, Changhee Lee, Fergus Imrie +2
Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, indepen…
Fourier Neural Differential Equations for learning Quantum Field Theories
Isaac Brant, Alexander Norcliffe, Pietro Liò
A Quantum Field Theory is defined by its interaction Hamiltonian, and linked to experimental data by the scattering matrix. The scattering matrix is calculated as a perturbative se…
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
Alexander Norcliffe, Marc Peter Deisenroth
Neural ODEs demonstrate strong performance in generative and time-series modelling. However, training them via the adjoint method is slow compared to discrete models due to the req…
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…