Model Degeneracy

Model degeneracy, the existence of multiple distinct model parameters yielding similar outputs, is a significant challenge across diverse machine learning applications. Current research focuses on understanding and mitigating degeneracy in various model architectures, including recurrent neural networks, large language models, and those used in quantitative MRI and robotic perception, often employing techniques like overgenerate-and-rank methods and data distribution design to improve model robustness and interpretability. Addressing model degeneracy is crucial for enhancing the reliability and trustworthiness of machine learning systems, improving the accuracy of scientific inferences, and enabling more robust autonomous systems.