On the forward invariance of Neural ODEs: performance guarantees for policy learning
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Updated
Aug 27, 2024 - Python
On the forward invariance of Neural ODEs: performance guarantees for policy learning
This repo is the official implementation for the series of works on (Path-dependent) Neural Jump ODEs.
Python tools for non-intrusive reduced order modeling
Lagrangian and Hamiltonian Neural Ordinary Differential Equations (NODEs)
Accompanying code for the paper "Amortized reparametrization: efficient and scalable variational inference for latent SDEs
Neural Ordinary Differential Equations for Reinforcement Learning
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"
[TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".
Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (NeurIPS 2022)
Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"
An implementation of Neural ODEs in PyTorch.
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
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.
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