Ashish Kumar Singh

Machine learning accelerates polaron hopping calculations by intelligently navigating complex electronic landscapes, yielding reproducible migration barriers with significantly reduced first-principles sampling.

Standard early-exit architectures collapse completely outside their pre-selected exits, but stochastic prefix supervision transforms a normal Transformer into a valid, deployable language model at literally every intermediate layer depth.