27 citations · 40 across the 6 of their papers we have counts for
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cs.LG2021★ 2 cited
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…
cs.LG2021★ 8 cited
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…
cs.LG2019★ 1 cited
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…