patrick-kidger / NeuralCDE
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
☆627Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for NeuralCDE
- Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.☆421Updated last year
- Code for "Latent ODEs for Irregularly-Sampled Time Series" paper☆519Updated 3 years ago
- Pytorch implementation of Augmented Neural ODEs☆532Updated last year
- Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations☆692Updated 8 months ago
- Pytorch implementation of GRU-ODE-Bayes☆227Updated 2 years ago
- Code repository of the paper Learning Long-Term Dependencies in Irregularly-Sampled Time Series☆111Updated last year
- A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods☆1,399Updated 6 months ago
- Code for the paper "Learning Differential Equations that are Easy to Solve"☆270Updated 2 years ago
- A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical…☆1,346Updated 2 months ago
- code for "FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models".☆629Updated 4 years ago
- Differentiable computations of the signature and logsignature transforms, on both CPU and GPU. (ICLR 2021)☆262Updated 10 months ago
- This repository contains experiments with Neural Ordinary Differential Equations with simulated and real empirical data☆196Updated 5 years ago
- Differentiable SDE solvers with GPU support and efficient sensitivity analysis.☆1,587Updated 5 months ago
- Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)☆115Updated 3 years ago
- Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support☆218Updated 4 years ago
- PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks☆425Updated 2 months ago
- Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"☆725Updated last year
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks☆123Updated 2 months ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆170Updated 2 years ago
- TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155☆127Updated last year
- This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a…☆301Updated 3 weeks ago
- [IJCAI'19, NeurIPS'19] Anode: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs☆104Updated 4 years ago
- ☆235Updated last year
- Normalizing flows in PyTorch☆848Updated last year
- [NeurIPS'19] Deep Equilibrium Models☆727Updated 2 years ago
- Linear and non-linear spectral forecasting algorithms☆133Updated 3 years ago
- This repository contains code for the paper: https://arxiv.org/abs/1905.03806. It also contains scripts to reproduce the results in the p…☆167Updated 4 years ago
- Constrained optimization toolkit for PyTorch☆661Updated 2 years ago
- N-BEATS is a neural-network based model for univariate timeseries forecasting. N-BEATS is a ServiceNow Research project that was started …☆516Updated 2 years ago
- Neural Graph Differential Equations (Neural GDEs)☆190Updated 3 years ago