no code implementations • 21 Mar 2023 • Emile Reyn Engelbrecht, Johan du Preez
More formally, bad-looking samples lie in the complementary space, which is the area between and around the boundaries of the labelled categories within the classifier's embedding space.
no code implementations • 23 Jun 2022 • Werner van der Merwe, Herman Kamper, Johan du Preez
In this paper, we present an extension to LDA that uses a Markov chain to model temporal information.
no code implementations • 7 Oct 2021 • 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.
no code implementations • 5 Oct 2021 • Simon Streicher, Johan du Preez
We present a means of formulating and solving graph coloring problems with probabilistic graphical models.
1 code implementation • 30 Sep 2021 • Simon Streicher, Johan du Preez
It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up.
no code implementations • 23 Oct 2019 • Felix McGregor, Arnu Pretorius, Johan du Preez, Steve Kroon
Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs.
no code implementations • 8 Apr 2013 • 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 recognition and machine learning algorithms.