arXiv subjects

Christopher Cleghorn

Publications and source records attributed to Christopher Cleghorn.

4 recordsLinked to original sources

arXiv2026

Prospecting MeerKAT Continuum Data for Enigmatic Radio Sources with Unsupervised Vector-Quantised Variational Autoencoders

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 Galaxy Cluster Legacy Survey (MGCLS) and compare their performance to other machine learning methods. We examine the effectiveness of VQ-VAEs in identifying radio continuum sources with anomalous structures in the image-plane domain. We compare performance to a supervised training set and other published anomaly detection methods, focusing especially on simple autoencoders (AE), Memory Unit Autoencoders (MUAE) and a human-in-the-loop software Astronomaly based on bootstrap your own latent (BYOL). Our investigations show that VQ-VAE perform better than simple AEs, are not as accurate as MUAE but are much faster and are also faster and comparable to Astronomaly while not requiring labelled data. We observe that they are able to remove a majority of the typical sources in such data, even when trained in an unsupervised manner on unlabelled data. We also provide our testing set of a large sample of manually labelled radio sources, in particular radio galaxies, taken from the MGCLS at 1.28 GHz. We find VQ-VAEs to be potentially useful as part of automated approaches towards searching through high volumes of data which are key in extracting the full scientific potential of the Square Kilometre Array and its pathfinders.

astro-ph.IM↗

arXiv2023

LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization

Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. Here, we propose using the coding abilities of LLMs to introduce meaningful variations to code defining neural networks. Meanwhile, Quality-Diversity (QD) algorithms are known to discover diverse and robust solutions. By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce \texttt{LLMatic}, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, \texttt{LLMatic} uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and high-performing networks. We test \texttt{LLMatic} on the CIFAR-10 and NAS-bench-201 benchmarks, demonstrating that it can produce competitive networks while evaluating just $2,000$ candidates, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark. The open-sourced code is available in \url{https://github.com/umair-nasir14/LLMatic}.

cs.NE↗

arXiv2023

Training Feedforward Neural Networks with Bayesian Hyper-Heuristics

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 of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process.

cs.LG↗

arXiv2022

Towards Objective Metrics for Procedurally Generated Video Game Levels

With increasing interest in procedural content generation by academia and game developers alike, it is vital that different approaches can be compared fairly. However, evaluating procedurally generated video game levels is often difficult, due to the lack of standardised, game-independent metrics. In this paper, we introduce two simulation-based evaluation metrics that involve analysing the behaviour of an A* agent to measure the diversity and difficulty of generated levels in a general, game-independent manner. Diversity is calculated by comparing action trajectories from different levels using the edit distance, and difficulty is measured as how much exploration and expansion of the A* search tree is necessary before the agent can solve the level. We demonstrate that our diversity metric is more robust to changes in level size and representation than current methods and additionally measures factors that directly affect playability, instead of focusing on visual information. The difficulty metric shows promise, as it correlates with existing estimates of difficulty in one of the tested domains, but it does face some challenges in the other domain. Finally, to promote reproducibility, we publicly release our evaluation framework.

cs.AI↗