BorealisAI / continuous-time-flow-process
PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
☆48Updated 4 years ago
Alternatives and similar repositories for continuous-time-flow-process:
Users that are interested in continuous-time-flow-process are comparing it to the libraries listed below
- Official code for the ICLR 2021 paper Neural ODE Processes☆72Updated 2 years ago
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆58Updated 4 years ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆47Updated last year
- Light-weighted code for Orthogonal Additive Gaussian Processes☆41Updated 8 months ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Experiments for Neural Flows paper☆94Updated 3 years ago
- Code for "Causal autoregressive flows" - AISTATS, 2021☆44Updated 3 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- code for "Neural Jump Ordinary Differential Equations"☆29Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆58Updated 6 months ago
- Normalizing Flows with a resampled base distribution☆46Updated 2 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Methods and experiments for assumed density SDE approximations☆11Updated 3 years ago
- Differentiable computations for the signature-PDE-kernel on CPU and GPU.☆53Updated 10 months ago
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆41Updated 2 years ago
- Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch☆22Updated 5 years ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆82Updated last year
- ☆13Updated last year
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆32Updated 2 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- This repository contains code for applying Riemannian geometry in machine learning.☆77Updated 3 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Example applications of path signatures☆38Updated 2 weeks ago
- PyTorch implementation of Stein Variational Gradient Descent☆43Updated last year
- Code for our paper: Online Variational Filtering and Parameter Learning☆18Updated 3 years ago
- ☆38Updated 4 years ago