Andrew Cheng

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks

This work expresses the known accessibility transition in an equivalent conic form, centered for compact convex targets at the statistical dimension of the polar cone, and introduces Random Mapping Networks (RaMaN), which instantiate the predicted latent dimension using structured Hadamard or seed-regenerated Gaussian maps.