Stanford U & Google’s ResMem Improves Neural Network Models’ Generalization via Explicit Memorization | Synced

In the new paper ResMem: Learn What You Can and Memorize the Rest, a Stanford University research team proposes the residual-memorization (ResMem) algorithm, a novel approach to improving the generalization ability of neural network models by performing explicit memorization via a separate k-nearest neighbour component.