AWehenkel / Graphical-Normalizing-Flows
Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tractable Jacobian.
☆45Updated last year
Alternatives and similar repositories for Graphical-Normalizing-Flows:
Users that are interested in Graphical-Normalizing-Flows are comparing it to the libraries listed below
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Library for normalizing flows and neural flows.☆24Updated 2 years ago
- We got a stew going!☆27Updated last year
- Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling☆36Updated 3 years ago
- Normalizing Flows with a resampled base distribution☆44Updated 2 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 4 years ago
- PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows☆31Updated 3 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆17Updated 3 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- Normalizing Flows using JAX☆82Updated last year
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- orbital MCMC☆10Updated 3 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆23Updated 2 years ago
- Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN): https://arxiv.org/abs/1907.08982☆21Updated 4 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆70Updated 2 years ago
- Relative gradient optimization of the Jacobian term in unsupervised deep learning, NeurIPS 2020☆21Updated 3 years ago
- Sequential Neural Likelihood☆39Updated 5 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- Riemannian Convex Potential Maps☆67Updated last year
- ☆53Updated 6 months ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆41Updated last year
- ☆15Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Deterministic particle dynamics for simulating Fokker-Planck probability flows☆24Updated last year
- Methods and experiments for assumed density SDE approximations☆11Updated 3 years ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- Official repository for "Categorical Normalizing Flows via Continuous Transformations"☆56Updated 3 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago