Cartesian Product Extrapolation

Cartesian product extrapolation in machine learning focuses on improving model performance when encountering inputs outside the range of the training data. Current research investigates this challenge across various model architectures, including neural networks, transformers, and deep operator networks, employing techniques like Anderson extrapolation, linear extrapolation of features, and multiple kernel learning to enhance extrapolation capabilities. This research is crucial for improving the reliability and generalizability of AI models in diverse applications, from materials science and drug discovery to natural language processing and computer vision, where extrapolation is often unavoidable and can significantly impact the accuracy and trustworthiness of predictions.