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Found 9 papers, 7 papers with code
Fourier Neural Differential Equations for learning Quantum Field Theories
A Quantum Field Theory is defined by its interaction Hamiltonian, and linked to experimental data by the scattering matrix.
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature
In this paper, we propose an alternative way to speed up the training of neural ODEs.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
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.
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure.
Learning Feynman Diagrams using Graph Neural Networks
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.
Composite Feature Selection using Deep Ensembles
To do so, we define predictive groups in terms of linear and non-linear interactions between features.
Meta-learning using privileged information for dynamics
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.
Neural ODE Processes
To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.
On Second Order Behaviour in Augmented Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.
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