Viewing Neural Networks Through a Statistical-Physics Lens
Machine-learning technologies have profoundly reshaped many technical fields, with sweeping applications in medical diagnosis, customer service, drug discovery, and beyond. Central to this transformation are neural networks (NNs), models that learn patterns from data by combining many simple computational units, or neurons, linked by weighted connections. Acting collectively, these neurons can process data to learn complex input–output relationships. Despite their practical success, the fundamental mechanisms by which NNs learn remain poorly understood at a theoretical level. Statistical physics offers a promising framework for exploring central questions in machine-learning theory, potentially clarifying how learning depends on the layout of the network—the NN architecture—and on statistics of the data—the data structure . Three recent papers in a special Physical Review E collection (See Collection: Statistical Physics Meets Machine Learning - Machine Learning Meets Statistical Physi...