A Theory of Deep Learning

Deep learning works remarkably well despite violating classical statistical theory, and now there's a mathematical explanation. By analyzing neural networks through output space rather than parameters, researchers show that training separates signal from noise — generalizable patterns get learned while memorized noise becomes invisible at test time, explaining why overfit models still perform well on new data.