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The Collapse Of Deep Learning Theory
A 2019 research paper further dismantled the theoretical framework of statistical learning. It built on 2016 findings by Zhang et al., proving that neural networks memorize random labels despite their supposed complexity. This gap between theory and practice leaves practitioners without a formal guarantee of how models generalize to new data.