random-low-dimensional-reparameterization

This paper analyzes when random low-dimensional reparameterizations can train neural networks, deriving an orientation-resolved master formula for the random-slice residual and introducing RaMaN, a scalable framework that predicts required latent dimensions while dramatically reducing memory costs.