stefanradev93 / cINNLinks
Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"
☆24Updated 5 years ago
Alternatives and similar repositories for cINN
Users that are interested in cINN are comparing it to the libraries listed below
Sorting:
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 6 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆124Updated 2 years ago
- ☆11Updated 4 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆26Updated 5 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Sequential Neural Likelihood☆42Updated 6 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 7 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆29Updated 11 months ago
- Code for "Variational Autoencoder with Learned Latent Structure"☆34Updated 4 years ago
- Code for ICML 2019 paper on "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"☆19Updated 5 years ago
- ☆17Updated 5 years ago
- Parametric Gaussian Process Regression for Big Data☆45Updated 5 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆45Updated 7 years ago
- ☆30Updated 3 years ago
- Official code for Coupled Oscillatory RNN (ICLR 2021, Oral)☆53Updated 4 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- Pytorch implementation of Planar Flow☆17Updated 6 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆35Updated 4 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆57Updated last year
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆78Updated 2 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 3 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆25Updated last year
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 7 years ago
- A toolbox for inference of mixture models☆16Updated 2 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆21Updated 11 years ago