64 Open source projects that are alternatives of or similar to Bayes Nn

A list of awesome resources on normalizing flows.

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

Julia package for automatic Bayesian inference on a factor graph with reactive message passing

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Seminars DeepBayes Summer School 2018

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

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

PixelVAE with or without regularization

GPstuff - Gaussian process models for Bayesian analysis

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

PyTorch implementation of normalizing flow models

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

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

Statistical Rethinking (2nd ed.) with NumPyro

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

DGMs for NLP. A roadmap.

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

Tensorflow implementation of conditional variational auto-encoder for MNIST

Automatic Reparameterisation of Probabilistic Programs

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

A Latent Dirichlet Allocation implementation in Python.

Modular Probabilistic Programming on MXNet

We got a stew going!

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

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Demultiplexing pooled scRNA-seq data with or without genotype reference

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

Learning about Haskell with Variational Autoencoders

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)

Lagrangian VAE

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

Natural Gradient, Variational Inference

Collection of probabilistic models and inference algorithms

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..

Implementation of VLAE

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

Statistical Rethinking (2nd Ed) with Tensorflow Probability

CmdStanR: the R interface to CmdStan

Scalable inference for a generative model of astronomical images

Code for the paper Implicit Weight Uncertainty in Neural Networks

Understanding normalizing flows

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

PyTorch implementation of "Weight Uncertainty in Neural Networks"

A toy model of Friston's active inference in Tensorflow

Kalman Variational Auto-Encoder

Implementation of Sequential Attend, Infer, Repeat (SQAIR)

Gaussian processes in TensorFlow

A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"

Adversarial Imitation Via Variational Inverse Reinforcement Learning

Efficient, lightweight variational inference and approximation bounds

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

Regression with Summary Statistics.

Deep universal probabilistic programming with Python and PyTorch

PyTorch Implementations of Dropout Variants

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

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

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

Ladder Variational Autoencoders (LVAE) in PyTorch

Deep active inference agents using Monte-Carlo methods

Bayesian mixture models for estimating and clustering cancer cell fractions