There are 1 repository under approximate-inference topic.
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
DGMs for NLP. A roadmap.
Probabilistic Programming with Gaussian processes in Julia
Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
A curated list of resources about Machine Learning for Robotics
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
A Python package for approximate Bayesian inference and optimization using Gaussian processes
Implementations of the ICML 2017 paper (with Yarin Gal)
Input Inference for Control (i2c), a control-as-inference framework for optimal control
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Variational Bayesian decision-making for continuous utilities
An implementation of loopy belief propagation for binary image denoising. Both sequential and parallel updates are implemented.
Empirical analysis of recent stochastic gradient methods for approximate inference in Bayesian deep learning, including SWA-Gaussian, MultiSWAG, and deep ensembles. See report_localglobal.pdf.
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
Probabilistic approach to neural nets - modern scalable approximate inference methods
Denoise a given image using Loopy Belief Propagation
Correcting predictions for approximate Bayesian inference
This project implements both exact and approximate inference techniques for Bayesian Networks using enumeration and rejection sampling, respectively. It processes Bayesian Network structures in XMLBIF format, accepting command-line inputs to compute the posterior distribution of a query variable given observed evidence.
Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression
Code repository for the UAI 2020 paper "Active learning of conditional mean embeddings via Bayesian optimisation" by S. R. Chowdhury, R. Oliveira and F. Ramos.
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
FAIKR MOD3 project
My undergraduate honours project, with others' private information/code removed.