57 citations · 108 across the 20 of their papers we have counts for
24 papers
A Flexible and Generic Approach for Explainable Landscape Analysis and the pyXla Toolbox
Tony Ombaso, Anna S. Bosman, Arnaud Liefooghe +2
Landscape analysis has been successfully applied to understand complex optimisation problems, gain insights into algorithm behaviour, and automate algorithm selection and configura…
Restoring Neural Network Plasticity for Faster Transfer Learning
Xander Coetzer, Arné Schreuder, Anna Sergeevna Bosman
Transfer learning with models pretrained on ImageNet has become a standard practice in computer vision. Transfer learning refers to fine-tuning pretrained weights of a neural netwo…
Regularisation in neural networks: a survey and empirical analysis of approaches
Christiaan P. Opperman, Anna S. Bosman, Katherine M. Malan
Despite huge successes on a wide range of tasks, neural networks are known to sometimes struggle to generalise to unseen data. Many approaches have been proposed over the years to…
Prospecting MeerKAT Continuum Data for Enigmatic Radio Sources with Unsupervised Vector-Quantised Variational Autoencoders
Fernando L. Ventura, Kshitij Thorat, Anna Bosman +2
We present a novel application of Vector quantised variational autoencoders (VQ-VAEs) as an unsupervised tool to probe deep 1.28 GHz radio continuum images taken from the MeerKAT G…
Online Meta-learning for AutoML in Real-time (OnMAR)
Mia Gerber, Anna Sergeevna Bosman, Johan Pieter de Villiers
Automated machine learning (AutoML) is a research area focusing on using optimisation techniques to design machine learning (ML) algorithms, alleviating the need for a human to per…
Hilbert curves for efficient exploratory landscape analysis neighbourhood sampling
Johannes J. Pienaar, Anna S. Bosman, Katherine M. Malan
Landscape analysis aims to characterise optimisation problems based on their objective (or fitness) function landscape properties. The problem search space is typically sampled, an…
Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural Networks
Reinhard Booysen, Anna Sergeevna Bosman
Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) ser…
Distal Interference: Exploring the Limits of Model-Based Continual Learning
Heinrich van Deventer, Anna Sergeevna Bosman
Continual learning is the sequential learning of different tasks by a machine learning model. Continual learning is known to be hindered by catastrophic interference or forgetting,…
Empirical Loss Landscape Analysis of Neural Network Activation Functions
Anna Sergeevna Bosman, Andries Engelbrecht, Marde Helbig
Activation functions play a significant role in neural network design by enabling non-linearity. The choice of activation function was previously shown to influence the properties…
Convolutional neural networks for crack detection on flexible road pavements
Hermann Tapamo, Anna Bosman, James Maina +1
Flexible road pavements deteriorate primarily due to traffic and adverse environmental conditions. Cracking is the most common deterioration mechanism; the surveying thereof is typ…
Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
Arné Schreuder, Anna Bosman, Andries Engelbrecht +1
The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means…
Denoising Diffusion Post-Processing for Low-Light Image Enhancement
Savvas Panagiotou, Anna S. Bosman
Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image…
Genetic Micro-Programs for Automated Software Testing with Large Path Coverage
Jarrod Goschen, Anna Sergeevna Bosman, Stefan Gruner
Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly…
Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise
Thamsanqa Mlotshwa, Heinrich van Deventer, Anna Sergeevna Bosman
In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data. For example, the frequently used mean squared error (M…
Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models
Moseli Mots'oehli, Anna Sergeevna Bosman, Johan Pieter De Villiers
Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence…
ATLAS: Universal Function Approximator for Memory Retention
Heinrich van Deventer, Anna Bosman
Artificial neural networks (ANNs), despite their universal function approximation capability and practical success, are subject to catastrophic forgetting. Catastrophic forgetting…
A Local Optima Network Analysis of the Feedforward Neural Architecture Space
Isak Potgieter, Christopher W. Cleghorn, Anna S. Bosman
This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural archi…
KASAM: Spline Additive Models for Function Approximation
Heinrich van Deventer, Pieter Janse van Rensburg, Anna Bosman
Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is i…
Hybridised Loss Functions for Improved Neural Network Generalisation
Matthew C. Dickson, Anna S. Bosman, Katherine M. Malan
Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Spec…
Black-Box Saliency Map Generation Using Bayesian Optimisation
Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches…
Loss Surface Modality of Feed-Forward Neural Network Architectures
Anna Sergeevna Bosman, Andries Engelbrecht, Mardé Helbig
It has been argued in the past that high-dimensional neural networks do not exhibit local minima capable of trapping an optimisation algorithm. However, the relationship between lo…
Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions
Anna Sergeevna Bosman, Andries Engelbrecht, Mardé Helbig
Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural networ…
Evolutionary Neural Architecture Search for Image Restoration
Gerard Jacques van Wyk, Anna Sergeevna Bosman
Convolutional neural network (CNN) architectures have traditionally been explored by human experts in a manual search process that is time-consuming and ineffectively explores the…
Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks
Kevin Louis de Jong, Anna Sergeevna Bosman
This paper proposes an efficient unsupervised method for detecting relevant changes between two temporally different images of the same scene. A convolutional neural network (CNN)…