Large-Scale Learning with Less RAM via Randomization

Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young

Abstract: We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float...