pierresegonne / VINF
Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
☆25Updated 4 years ago
Alternatives and similar repositories for VINF:
Users that are interested in VINF are comparing it to the libraries listed below
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Variational Auto-Regressive Gaussian Processes for Continual Learning☆21Updated 3 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 6 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆38Updated 5 years ago
- Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN): https://arxiv.org/abs/1907.08982☆21Updated 4 years ago
- ☆30Updated 2 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆87Updated 2 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…☆45Updated last year
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- A pytorch version of hamiltonian monte carlo☆14Updated 5 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Code for Unbiased Implicit Variational Inference (UIVI)☆14Updated 6 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆23Updated 5 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'☆25Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Normalizing Flows using JAX☆83Updated last year
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- ☆36Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 2 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- ☆22Updated 4 years ago
- Recyclable Gaussian Processes☆11Updated 2 years ago
- Code required to reproduce the experiments in Auxiliary Variational MCMC☆17Updated 6 years ago
- Minimal Gaussian process library in JAX with a simple (custom) approach to state management.☆11Updated last year