22 citations · 34 across the 14 of their papers we have counts for
14 papers
A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites
Eliud Nyakweba Koto, Jaco du Toit, Adham Stoltz +1
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle r…
On the link between generative semi-supervised learning and generative open-set recognition
Emile Reyn Engelbrecht, Johan du Preez
This study investigates the relationship between semi-supervised learning (SSL, which is training off partially labelled datasets) and open-set recognition (OSR, which is classific…
Soft-Output Signal Detection for Cetacean Vocalizations Using Spectral Entropy, K-Means Clustering and the Continuous Wavelet Transform
Marco W. Rademan, Daniel J. Versfeld, Johan A. du Preez
Underwater acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in…
Rail break and derailment prediction using Probabilistic Graphical Modelling
Rebecca M. C. Taylor, Johan A. du Preez
Rail breaks are one of the most common causes of derailments internationally. This is no different for the South African Iron Ore line. Many rail breaks occur as a heavy-haul train…
SimLDA: A tool for topic model evaluation
Rebecca M. C. Taylor, Johan A. du Preez
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extractio…
A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery
Werner van der Merwe, Herman Kamper, Johan du Preez
Latent Dirichlet allocation (LDA) is widely used for unsupervised topic modelling on sets of documents. No temporal information is used in the model. However, there is often a rela…
LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates
J du Toit, J du Preez, R Wolhuter
We present a sequential Bayesian learning method for tracking non-stationary signal-to-noise ratios in LDPC codes using probabilistic graphical models. We represent the LDPC code a…
LDPC codes: comparing cluster graphs to factor graphs
J du Toit, J du Preez, R Wolhuter
We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between ra…
A derivation of variational message passing (VMP) for latent Dirichlet allocation (LDA)
Rebecca M. C. Taylor, Dirko Coetsee, Johan A. du Preez
Latent Dirichlet Allocation (LDA) is a probabilistic model used to uncover latent topics in a corpus of documents. Inference is often performed using variational Bayes (VB) algorit…
Temporal separation of whale vocalizations from background oceanic noise using a power calculation
Jacques van Wyk, Jaco Versfeld, Johan du Preez
The process of analyzing audio signals in search of cetacean vocalizations is in many cases a very arduous task, requiring many complex computations, a plethora of digital processi…
ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets
Rebecca M. C. Taylor, Johan A. du Preez
Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extractio…
Degenerate Gaussian factors for probabilistic inference
J. C. Schoeman, C. E. van Daalen, J. A. du Preez
In this paper, we propose a parametrised factor that enables inference on Gaussian networks where linear dependencies exist among the random variables. Our factor representation is…
Efficient approximations of the multi-sensor labelled multi-Bernoulli filter
S. C. J. Robertson, C. E. van Daalen, J. A. du Preez
In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be…
Open-set learning with augmented categories by exploiting unlabelled data
Emile R. Engelbrecht, Johan A. du Preez
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training…