stefanradev93 / cINN
Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"
☆23Updated 4 years ago
Alternatives and similar repositories for cINN
Users that are interested in cINN are comparing it to the libraries listed below
Sorting:
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- Pytorch implementation for "Particle Flow Bayes' Rule"☆14Updated 5 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆22Updated 11 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆29Updated 3 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Pytorch implementation of Planar Flow☆17Updated 5 years ago
- ☆19Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.☆41Updated 3 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆18Updated 2 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- ☆30Updated 2 years ago
- Official code for UnICORNN (ICML 2021)☆27Updated 3 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
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization☆41Updated 3 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆31Updated 3 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆23Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆76Updated last year
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 5 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Random feature latent variable models in Python☆22Updated last year