Multiple Interpolation

Multiple interpolation techniques generate intermediate data points between known data, aiming to improve model performance, robustness, and explainability across diverse applications. Current research focuses on developing novel interpolation methods, including those leveraging neural networks (like transformers and implicit neural representations), and applying them to diverse problems such as data augmentation, modeling dynamic systems (e.g., aneurysm progression), and improving the interpretability of machine learning models. These advancements offer significant potential for enhancing the accuracy and reliability of various machine learning models and improving the understanding of complex systems in fields ranging from medical imaging to natural language processing.