NN Hyperparameters

Neural network (NN) hyperparameters, settings that control the training process, are crucial for model performance and fairness, yet optimizing them remains a challenge. Current research focuses on automating hyperparameter selection, exploring the relationship between hyperparameters and model fairness, and understanding their impact on computational efficiency and uncertainty quantification across various architectures including fully connected networks, convolutional neural networks, and transformers. These efforts aim to improve the reliability, accuracy, and ethical implications of NN models in diverse scientific and engineering applications, from robotics to wildfire prediction.