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The Top 195 Variational Inference Open Source Projects on Github

Categories > Machine Learning > Variational Inference
Pyro ⭐ 7,216
Deep universal probabilistic programming with Python and PyTorch
Pymc ⭐ 6,197
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
Gpflow ⭐ 1,538
Gaussian processes in TensorFlow
Deepbayes 2018 ⭐ 983
Seminars DeepBayes Summer School 2018
Variational Autoencoder ⭐ 905
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Boltzmann Machines ⭐ 792
Boltzmann Machines in TensorFlow with examples
Pytorch Bayesiancnn ⭐ 757
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Bayesian Neural Networks ⭐ 554
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Awesome Vaes ⭐ 448
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Bayes Nn ⭐ 423
Lecture notes on Bayesian deep learning
Awesome Normalizing Flows ⭐ 415
A list of awesome resources on normalizing flows.
Probabilistic_unet ⭐ 400
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Probreg ⭐ 399
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
Rethinking Numpyro ⭐ 291
Statistical Rethinking (2nd ed.) with NumPyro
Generative_models_tutorial_with_demo ⭐ 268
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..
Deep Generative Models For Natural Language Processing ⭐ 262
DGMs for NLP. A roadmap.
Good Papers ⭐ 231
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
Variational Ladder Autoencoder ⭐ 198
Implementation of VLAE
Probabilistic Models ⭐ 196
Collection of probabilistic models and inference algorithms
Bcpd ⭐ 154
Bayesian Coherent Point Drift (BCPD/BCPD++)
Vbmc ⭐ 144
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
Tensorflow Mnist Cvae ⭐ 134
Tensorflow implementation of conditional variational auto-encoder for MNIST
Rethinking Tensorflow Probability ⭐ 127
Statistical Rethinking (2nd Ed) with Tensorflow Probability
Celeste.jl ⭐ 124
Scalable inference for a generative model of astronomical images
Gpstuff ⭐ 120
GPstuff - Gaussian process models for Bayesian analysis
Normalizing Flows ⭐ 108
Understanding normalizing flows
Variational Inference With Normalizing Flows ⭐ 106
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
Kvae ⭐ 94
Kalman Variational Auto-Encoder
Mxfusion ⭐ 93
Modular Probabilistic Programming on MXNet
Sqair ⭐ 89
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
Gelato ⭐ 85
Bayesian dessert for Lasagne
Sequential Variational Autoencoder ⭐ 71
Implementation of Sequential Variational Autoencoder
Cmdstanr ⭐ 69
CmdStanR: the R interface to CmdStan
Inverse_rl ⭐ 63
Adversarial Imitation Via Variational Inverse Reinforcement Learning
Generalized Pixelvae ⭐ 61
PixelVAE with or without regularization
Dropouts ⭐ 57
PyTorch Implementations of Dropout Variants
Noisynaturalgradient ⭐ 56
TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC"
Bayesbyhypernet ⭐ 55
Code for the paper Implicit Weight Uncertainty in Neural Networks
Tf Var Attention ⭐ 55
Tensorflow Implementation of Variational Attention for Sequence to Sequence Models (COLING 2018)
Dun ⭐ 52
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Topic Rnn ⭐ 47
Implementation (in progress) of Dieng et al.'s TopicRNN: a neural topic model & RNN hybrid.
Vib Pytorch ⭐ 46
Pytorch implementation of Deep Variational Information Bottleneck
Ai_learning_hub ⭐ 45
AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
Normalizing Flows ⭐ 44
PyTorch implementation of normalizing flow models
Pylda ⭐ 44
A Latent Dirichlet Allocation implementation in Python.
Probai 2021 Pyro ⭐ 42
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)
Wae Rnf Lm ⭐ 40
Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" https://arxiv.org/abs/1904.02399
Sivi ⭐ 39
Use neural network to construct expressive hierarchical distribution; Optimizing a novel bound with theoretical guarantees; A fast and robust method with accurate uncertainty estimation. (ICML 2018)
Rss ⭐ 38
Regression with Summary Statistics.
Gp Infer Net ⭐ 35
Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
Artificial_neural_networks ⭐ 35
A collection of Methods and Models for various architectures of Artificial Neural Networks
Rnn Vae ⭐ 34
Variational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"
Mvae ⭐ 33
Mixed-curvature Variational Autoencoders (ICLR 2020)
Ladder Vae Pytorch ⭐ 31
Ladder Variational Autoencoders (LVAE) in PyTorch
Vae Style Transfer ⭐ 30
An experiment in VAE-based artistic style transfer by embedding fiddling.
Deep Active Inference Mc ⭐ 28
Deep active inference agents using Monte-Carlo methods
Viforsdes ⭐ 25
A variational method for fast, approximate inference for stochastic differential equations.
Autogp ⭐ 24
Code for AutoGP
Vgrnn ⭐ 24
Variational Graph Recurrent Neural Networks - PyTorch
Viabel ⭐ 24
Efficient, lightweight variational inference and approximation bounds
Active Inference ⭐ 23
A toy model of Friston's active inference in Tensorflow
Vinf ⭐ 23
Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
Lagvae ⭐ 22
Lagrangian VAE
Lgamma ⭐ 21
Implementations of polygamma, lgamma, and beta functions for PyTorch
Selsum ⭐ 21
Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.
Autoreparam ⭐ 20
Automatic Reparameterisation of Probabilistic Programs
Noisy K Fac ⭐ 20
Natural Gradient, Variational Inference
Voila ⭐ 19
Variational Inference for Langevin Equations
Vireo ⭐ 19
Demultiplexing pooled scRNA-seq data with or without genotype reference
Gradient Boosted Normalizing Flows ⭐ 18
We got a stew going!
Variational_gradient_matching_for_dynamical_systems ⭐ 18
Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"
Posterior Collapse List ⭐ 17
A curated list of techniques to avoid posterior collapse
Variationalautoencoders ⭐ 17
Comparison of Variational Autoencoders with Bayesian Neural Networks. Accuracy, Latent space, Reconstruction and White Noise filtering.
Trlda ⭐ 17
Implementations of various online inference algorithms for LDA, with Python interface.
Haskell Vae ⭐ 17
Learning about Haskell with Variational Autoencoders
Bayesianrnn ⭐ 16
Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.
Ccube ⭐ 16
Bayesian mixture models for estimating and clustering cancer cell fractions
Adaptive F Divergence ⭐ 16
A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"
Phylostan ⭐ 15
Phylogenetic inference using Stan
Machine Learning Summer Schools ⭐ 14
Curated materials for different machine learning related summer schools
Boundary Gp ⭐ 14
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Prosper ⭐ 14
A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Stein Variational Samplers ⭐ 14
Python and MATLAB code for Stein Variational sampling methods
Tf2 Vaeac ⭐ 13
Variational Autoencoder with Arbitrary Conditioning for Image Inpainting in TensorFlow 2.0
Bayesian Methods For Machine Learning ⭐ 13
Bayesian Methods for Machine Learning
Cikm18 Lcva ⭐ 12
Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
Amortized_svgd ⭐ 11
Experiments of amortized stein variational gradient
Binary Vae ⭐ 11
A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.
Sentencemim Demo ⭐ 11
This repo contains code to reproduce some of the results presented in the paper "SentenceMIM: A Latent Variable Language Model"
Mcdo ⭐ 11
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
Locus ⭐ 10
locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression
Zhusuan Pytorch ⭐ 10
An Elegant Library for Bayesian Deep Learning
Melissa ⭐ 10
Bayesian Clustering and Imputation of Single Cell Methylomes
Pytorch Vae ⭐ 10
Variational Autoencoder in Pytorch
Mim ⭐ 10
Code for "MIM: Mutual Information Machine" paper.
Poissonmf_cs ⭐ 10
Code for "Content-Based Social Recommendation with Poisson Matrix Factorization" (ECML-PKDD 2017)
Avuc ⭐ 10
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
Graph Generation Vi ⭐ 10
This repository contains PyTorch implementation of the following paper: "Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation"
Attend Infer Repeat Pytorch ⭐ 9
Attend Infer Repeat (AIR) in PyTorch
Deep_variational_information_bottleneck ⭐ 9
Tensorflow implementation of deep variational information bottleneck
1-100 of 195 projects

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Application Programming Interfaces 📦 120
Applications 📦 181
Artificial Intelligence 📦 72
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Cloud Computing 📦 79
Code Quality 📦 28
Collaboration 📦 30
Command Line Interface 📦 48
Community 📦 81
Companies 📦 60
Compilers 📦 60
Computer Science 📦 74
Configuration Management 📦 39
Content Management 📦 167
Control Flow 📦 197
Data Formats 📦 77
Data Processing 📦 266
Data Storage 📦 132
Economics 📦 60
Frameworks 📦 198
Games 📦 122
Graphics 📦 103
Hardware 📦 148
Integrated Development Environments 📦 47
Learning Resources 📦 147
Legal 📦 28
Libraries 📦 119
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Mapping 📦 61
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Operations 📦 120
Package Managers 📦 52
Programming Languages 📦 229
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Software Architecture 📦 70
Software Development 📦 68
Software Performance 📦 57
Software Quality 📦 127
Text Editors 📦 45
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User Interface Components 📦 465
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Web Servers 📦 25
Web User Interface 📦 194
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