5 citations · 11 across the 9 of their papers we have counts for
11 papers
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…
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Alexander Norcliffe, Bogdan Cebere, Fergus Imrie +2
Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods f…
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Alexander Norcliffe, Lev Proleev, Diana Mincu +3
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for m…