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Showing 1 to 6 of 6 for “"Hyperparameter Optimisation"”.
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Bayesian Optimisation of Hyperparameters in Regression Models for Smart Energy and Environmental Systems
… forecasting. The results demonstrate that hyperparameter optimisation significantly improves predictive performance and computational efficiency. For example, optimisation of artificial neural network models improved prediction accuracy for smart home energy consumption from 60\% to …
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Advanced computational techniques for pipe burst detection and localisation in water distribution networks
… first phase conducts a comparative analysis of hyperparameter optimisation techniques, Particle Swarm Optimisation (PSO) and Population-Based Training (PBT), for FL-DL architectures. This investigation addresses the limitations of traditional detection methods, which are often costly, …
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Automated Machine Learning for Predicting Trends in Time Series Data
… prediction research, the Algorithm Selection and Hyperparameter Optimisation (ASHO) is performed manually. However, manual ASHO is expensive and often results in a sub-optimal or mediocre model because it needs extensive experimentation as well as domain specific and Machine Learning (ML) expert …
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Optimising the Optimiser: Meta NeuroEvolution for Artificial Intelligence Problems
… fully solve a task in order to evaluate a set of hyperparameter values, conventional hyperparameter tuning methods can be highly sample inefficient and computationally expensive. Many widely used reinforcement learning architectures originate from scientific papers which include optimal …
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Advances in Meta-Learning, Robustness, and Second-Order Optimisation in Deep Learning
… can improve learning by solving the underlying optimisation problem more efficiently. Machine learning methods are typically very data hungry. Although modern machine learning has been hugely effective in solving real-world problems, these success stories are largely limited to settings where …
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Advances in Optimisation of Model Parameters and Hyperparameters for Neural Networks
… to minimise some loss metric, so the chosen optimisation algorithm plays a fundamental role in the training process — both through the optimisation logic itself, and the auxiliary *hyperparameters* which configure the optimiser’s behaviour. Moreover, Machine Learning tasks often demand unique …