MaximeVandegar / Normalizing-FlowsLinks
A repository to learn about Flows, mostly from papers
☆22Updated 4 years ago
Alternatives and similar repositories for Normalizing-Flows
Users that are interested in Normalizing-Flows are comparing it to the libraries listed below
Sorting:
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Sliced Iterative Generator (SIG) & Gaussianizing Iterative Slicing (GIS)☆36Updated 2 years ago
- Code for Gaussian Score Matching Variational Inference☆33Updated 3 months ago
- Normalizing Flows using JAX☆83Updated last year
- code supplement for variational boosting (https://arxiv.org/abs/1611.06585)☆11Updated 7 years ago
- ☆52Updated 2 years ago
- Pytorch implementation of Block Neural Autoregressive Flow☆178Updated 3 years ago
- Conditional density estimation with neural networks☆31Updated 4 months ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Experiments for the Neural Autoregressive Flows paper☆124Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Manifold-learning flows (ℳ-flows)☆229Updated 4 years ago
- Gaussian Processes for Sequential Data☆18Updated 4 years ago
- ☆180Updated 5 years ago
- Masked Autoregressive Flow☆213Updated 10 months ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆101Updated last year
- Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling☆36Updated 3 years ago
- Tutorial materials of the Probabilistic Numerics Spring School.☆34Updated 2 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 5 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆103Updated last year
- ☆26Updated last year
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- Probabilistic modeling of tabular data with normalizing flows.☆56Updated 2 weeks ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- ☆53Updated 10 months ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆84Updated 4 years ago
- ☆23Updated 3 years ago