170 Open source projects that are alternatives of or similar to normalizing-flows

We got a stew going!

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

Lagrangian VAE

Kalman Variational Auto-Encoder

Tensorflow implementation of conditional variational auto-encoder for MNIST

Understanding normalizing flows

Learning about Haskell with Variational Autoencoders

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"

Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"

Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Boltzmann Machines in TensorFlow with examples

No description or website provided.

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Lecture notes on Bayesian deep learning

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB

Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

Efficient, lightweight variational inference and approximation bounds

Neural Relation Understanding: neural cardinality estimators for tabular data

Statistical Rethinking (2nd ed.) with NumPyro

Bayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available

Ladder Variational Autoencoders (LVAE) in PyTorch

Deep universal probabilistic programming with Python and PyTorch

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

Scalable inference for a generative model of astronomical images

Interactive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.

CmdStanR: the R interface to CmdStan

GPstuff - Gaussian process models for Bayesian analysis

Implementation of Sequential Attend, Infer, Repeat (SQAIR)

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

PyTorch Implementations of Dropout Variants

Automatic Reparameterisation of Probabilistic Programs

PyTorch implementation of "Weight Uncertainty in Neural Networks"

[ WSDM '19 ] Sequential Variational Autoencoders for Collaborative Filtering

A list of awesome resources on normalizing flows.

Modular Probabilistic Programming on MXNet

Deep active inference agents using Monte-Carlo methods

PixelVAE with or without regularization

Collection of probabilistic models and inference algorithms

Natural Gradient, Variational Inference

Gaussian processes in TensorFlow

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Seminars DeepBayes Summer School 2018

A collection of Methods and Models for various architectures of Artificial Neural Networks

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

Adversarial Imitation Via Variational Inverse Reinforcement Learning

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

Implementation of VLAE

PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)

Variational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"

Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen

DGMs for NLP. A roadmap.

Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019