This repository provides a collection of tutorials demonstrating how to implement SVGD using Python and PyTorch. It includes toy examples of vanilla SVGD and annealed SVGD applied to problems ranging ...

Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

This repository contains a step-by-step Jupyter notebook tutorial that walks you through building a deep active inference agent from scratch in a simple 2D gridworld with noisy observations. If you ...

Abstract: Pairwise learning underpins implicit collaborative filtering, yet its effectiveness is often hindered by sparse supervision, noisy interactions, and popularity-driven exposure bias. In this ...

Abstract: This paper proposes a variational Bayesian inference (VBI) based algorithm for gridless and online estimation of multiple two-dimensional directions of arrival (2D-DOAs), whose number and ...