Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Model evaluation measures how well a trained machine learning model performs on unseen data, while validation guides tuning during development. Best practice involves splitting data into training, ...

Physicists at Harvard University have developed a simplified, physics-inspired mathematical model to better understand how neural networks learn, potentially explaining why large AI systems often ...

Overview: Machine learning systems analyze massive datasets to identify patterns and automate complex digital decision-making ...