Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
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Updated
Dec 9, 2021 - Python
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"
Neural Ordinary Differential Equations for Reinforcement Learning
Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (NeurIPS 2022)
Python tools for non-intrusive reduced order modeling
Official implementation of the papers: Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs; Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
On the forward invariance of Neural ODEs: performance guarantees for policy learning
Accompanying code for the paper "Amortized reparametrization: efficient and scalable variational inference for latent SDEs
PINEURODEs is a repository collecting CMS group research work on the application of neural (stochastic/ordinary) differential equations and physically-informed neural networks to model complex multiscale systems.
NeuralODEs for Brain Tumor Segmentation. Implementation of Neural ODE for Glioma Segmentation Paper on BraTS 2020 Dataset
An implementation of Neural ODEs in PyTorch.
Repository of my Master Thesis Project at TUM at the end of my Ecole Polytechnique's studies. It tackles the subject of "Continuous Motion Interpolation with Neural Differential Equations"
Lagrangian and Hamiltonian Neural Ordinary Differential Equations (NODEs)
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