CAGE-NAS: Certified Functional Descent for Efficient Model Growth

Excerpt

arXiv:2610.01173v1 Announce Type: new Abstract: The progressive growth of neural networks requires deciding when the current representation remains sufficient for optimization and when it should be expanded. CAGE-NAS formulates this decision in function space through an admissibility criterion on approximations of the functional gradient. As long as a representation enables a certified Functional Gradient Descent step, the architecture remains fixed; when the criterion fails, a function-preservi