76 Open source projects that are alternatives of or similar to probai-2021-pyro

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

Materials of the Nordic Probabilistic AI School 2019.

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

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

Tensorflow implementation of conditional variational auto-encoder for MNIST

Microscopic Markov Chain Approach to model the spreading of COVID-19

Modular Probabilistic Programming on MXNet

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

Statistical Rethinking (2nd ed.) with NumPyro

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

Lecture notes on Bayesian deep learning

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

Demultiplexing pooled scRNA-seq data with or without genotype reference

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

A Latent Dirichlet Allocation implementation in Python.

GPstuff - Gaussian process models for Bayesian analysis

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)

Seminars DeepBayes Summer School 2018

Code for the paper Implicit Weight Uncertainty in Neural Networks

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

Separating Normalizing Flows code from Pyro and improving API

Boltzmann Machines in TensorFlow with examples

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

A list of awesome resources on normalizing flows.

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..

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Bayesian mixture models for estimating and clustering cancer cell fractions

Scalable inference for a generative model of astronomical images

Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]

Understanding normalizing flows

Blang's software development kit

PyTorch implementation of "Weight Uncertainty in Neural Networks"

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

Kalman Variational Auto-Encoder

PyTorch implementation of normalizing flow models

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

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"

Machine Learning models from scratch with a better visualisation

Deep universal probabilistic programming with Python and PyTorch

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

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

Regression with Summary Statistics.

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

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

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

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

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

Collection of probabilistic models and inference algorithms

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

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

Multi-Touch Attribution

Implementation of VLAE

Automatic Reparameterisation of Probabilistic Programs

A toy model of Friston's active inference in Tensorflow

A Python Library for Deep Probabilistic Modeling

DGMs for NLP. A roadmap.