270 Open source projects that are alternatives of or similar to autoreparam

DGMs for NLP. A roadmap.

GPstuff - Gaussian process models for Bayesian analysis

Boltzmann Machines in TensorFlow with examples

CmdStanR: the R interface to CmdStan

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

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)

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

Collection of probabilistic models and inference algorithms

Regression with Summary Statistics.

Summary of machine learning papers

Readings in Stream Processing

A GitHub repo for Quant Finance resources

Start with the Chempy tutorial

My notes on reinforcement learning papers

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

Full Bayesian Inference for Hidden Markov Models

A C++ library of Markov Chain Monte Carlo (MCMC) methods

List of Data Science and Machine Learning Resource that I frequently use

Application of the L2HMC algorithm to simulations in lattice QCD.

Material for a workshop on Bayesian stats with R

A collection of research and application papers of (uncertainty) calibration techniques.

Ladder Variational Autoencoders (LVAE) in PyTorch

Implementation of Sequential Attend, Infer, Repeat (SQAIR)

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

BAS R package https://merliseclyde.github.io/BAS/

PyTorch Implementations of Dropout Variants

Summarizing the papers I have read (Japanese)

List of awesome papers on Polarization Imaging

This repository hosts my reading notes for academic papers.

Natural Gradient, Variational Inference

Semi-Supervised Video Object Segmentation(VOS) Paper List

PhyML -- Phylogenetic estimation using (Maximum) Likelihood

Collection of books/papers that I've read/I'm going to read/I would remember that they exist/It is unlikely that I'll read/I'll never read.

Python MCMC Sampler

Papers about NN and linguistics

Zotero compatiblity scripts for papis

A curated list of all the Awesome --Topic Name-- lists I've found till date relevant to Data lifecycle, ML and DL.

A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering

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

PixelVAE with or without regularization

Pieces of code that have appeared on my blog with a focus on stochastic simulations.

Digital repository for the papers of a research organization.

A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.

We got a stew going!

Recent security papers on firmware analysis

Lagrangian VAE

This is a repository for summarizing papers especially related to machine learning.

FSL-Mate: A collection of resources for few-shot learning (FSL).

【仅作为交流学习使用】机器智能--相关书目及经典论文包括AutoML、情感分类、语音识别、声纹识别、语音合成实验代码等

Code for the paper Implicit Weight Uncertainty in Neural Networks

Efficient, lightweight variational inference and approximation bounds

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

Deep active inference agents using Monte-Carlo methods

Examples for Bayesian inference using DynamicHMC.jl and related packages.

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

Learning about Haskell with Variational Autoencoders

NLP 和机器学习论文中文翻译

A toy model of Friston's active inference in Tensorflow

Summary of Transformer applications for computer vision tasks.

Sequential Monte Carlo algorithm for approximation of posterior distributions.