Small Training

Small training data poses a significant challenge for many machine learning applications, hindering the development of accurate and robust models. Current research focuses on mitigating this limitation through techniques like data augmentation, innovative model architectures (including convolutional neural networks, transformers, and mixture-of-experts models), and novel algorithms such as gauge-optimal approximate learning and deep neuroevolution. These advancements aim to improve model performance and generalization capabilities even with limited training data, impacting diverse fields from medical image analysis and traffic prediction to environmental monitoring and industrial automation.