I'm interested in the concept of machine learning modularity, where systems are composed of independent modules that collaborate to tackle tasks. This modular approach offers adaptable inference, adjusting computation to task complexity rather than relying on rigid end-to-end models. It holds promise for improved systematic generalization beyond training data and fosters interpretability compared to black-box models. Moreover, modular systems are extensible and modules may be reused across various tasks and applications.
