Hossein Mobahi

HOPE reveals a transformative approach to deconstructing neural representations, enabling unbiased architectural decisions and efficient model compression without the need for data or hyperparameters.

DARKFormer closes the performance gap with exact softmax attention in finetuning by learning a data-aligned kernel geometry for efficient random feature approximation, sidestepping the need for retraining or large feature budgets.