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Showing 1 to 7 of 7 for “"hyperparameter selection"”.
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Hyperparameter Optimization of Opaque Models for Autonomous Vehicle Algorithms
Algorithms usually consist of many hyperparameters that need to be tuned to perform efficiently. It may be possible to tune a handful of parameters manually for simple algorithms however as the algorithm becomes more complex the number of hyper- parameters also increases which makes finding the …
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Optimizing AI Agents for Automated Software Engineering with Palimpzest
… in developing such systems is tuning agent hyperparameters— settings that affect performance such as choice of model, temperature settings, and context window sizes. As system complexity grows, the hyperparameter space expands, complicating optimization under real-world compute and time …
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Knowledge Distillation in DMRS CHEST ML to Optimize Radio Performance and Hardware Efficiency
… environments. It also explores the impact of hyperparameter selection on distillation quality. It investigates how different channel characteristics influence the knowledge transfer process while assessing computational efficiency through training duration measurements to provide a holistic …
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Crack identification through computer vision: from non-learning-based to learning-based methodologies, and from patch-level to pixel-level detections
… systematic study to investigate the impact from hyperparameter selection on the performance of deep convolutional neural network (DCNN) on roadway crack classification; iii) achieving pixel-level crack detection resolution on image data of real-world complexities through DCNN-based roadway crack …
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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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Learning Hyperparameters for Inverse Problems by Deep Neural Networks
… compared to existing regularization parameter selection methods. Numerical results for tomography demonstrate the potential benefits of using DNNs to learn regularization parameters.
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Neural networks for the prediction of chaos and turbulence
… part of the thesis, we focus on the network's hyperparameters, which markedly affect the performance of the machine. We optimise the procedure to select the hyperparameters, i.e. the validation strategy, in order to improve performance and robustness. First, we investigate common validation …