1 code implementation • 28 Nov 2023 • 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.
1 code implementation • 21 Aug 2023 • Alexander Norcliffe, Marc Peter Deisenroth
In this paper, we propose an alternative way to speed up the training of neural ODEs.
1 code implementation • 24 Feb 2023 • Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar
SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.
no code implementations • 15 Feb 2023 • Alexander Norcliffe, Lev Proleev, Diana Mincu, Fletcher Lee Hartsell, Katherine Heller, Subhrajit Roy
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure.
no code implementations • 25 Nov 2022 • Harrison Mitchell, Alexander Norcliffe, Pietro Liò
In the wake of the growing popularity of machine learning in particle physics, this work finds a new application of geometric deep learning on Feynman diagrams to make accurate and fast matrix element predictions with the potential to be used in analysis of quantum field theory.
2 code implementations • 1 Nov 2022 • Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar
To do so, we define predictive groups in terms of linear and non-linear interactions between features.
1 code implementation • ICLR Workshop Learning_to_Learn 2021 • Ben Day, Alexander Norcliffe, Jacob Moss, Pietro Liò
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.
2 code implementations • ICLR 2021 • Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò
To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.
1 code implementation • NeurIPS 2020 • Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Liò
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.
Ranked #22 on Image Classification on MNIST