dailyai.report

The 2016 paper by Zhang et al. challenged classical deep learning theory by demonstrating that models could memorize random labels. This finding shattered previous optimism regarding generalization bounds. Researchers now focus on why these models perform well despite theoretical gaps. It remains a foundational critique of early neural network theory.