On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
A new arXiv paper details the benefits of weight normalization (WN) for overparameterized matrix sensing problems. The research demonstrates that WN, when combined with Riemannian optimization, can achieve linear convergence, offering an exponential speedup compared to methods without WN. The analysis also indicates that increasing the level of overparameterization polynomially improves both iteration and sample complexity. AI