27 citations · 40 across the 6 of their papers we have counts for
6 papers
Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm
Simon Streicher, Johan du Preez
Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PG…
Automated Feature-Specific Tree Species Identification from Natural Images using Deep Semi-Supervised Learning
Dewald Homan, Johan A. du Preez
Prior work on plant species classification predominantly focuses on building models from isolated plant attributes. Hence, there is a need for tools that can assist in species iden…
A Probabilistic Graphical Model Approach to the Structure-and-Motion Problem
Simon Streicher, Willie Brink, Johan du Preez
We present a means of formulating and solving the well known structure-and-motion problem in computer vision with probabilistic graphical models. We model the unknown camera poses…
Graph Coloring: Comparing Cluster Graphs to Factor Graphs
Simon Streicher, Johan du Preez
We present a means of formulating and solving graph coloring problems with probabilistic graphical models. In contrast to the prevalent literature that uses factor graphs for this…
Stabilising priors for robust Bayesian deep learning
Felix McGregor, Arnu Pretorius, Johan du Preez +1
Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNN…
The PAV algorithm optimizes binary proper scoring rules
Niko Brummer, Johan du Preez
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern reco…