Model Stability

Model stability, the consistency of a model's predictions across different training runs or under minor input perturbations, is a critical concern across various machine learning applications. Current research focuses on identifying and mitigating instability in diverse architectures, including neural networks (particularly convolutional and recurrent types), graph neural networks, and neural ordinary differential equations, often employing techniques like ensembling, data augmentation, and regularization. Addressing model instability is crucial for ensuring reliable performance, reproducibility, and trustworthiness in applications ranging from climate modeling and medical image segmentation to natural language processing and risk stratification, ultimately improving the dependability and impact of machine learning systems.