Topic: feature learning

Jessica Howard presents optimal transport as a common language for neural-network ensembles, statistical field theories and exact renormalisation-group flows. Viewing a field theory as a probability distribution over functions makes transport methods useful for numerical nonperturbative calculations and analytical insights. The talk connects these ideas to particle-physics data analysis and to feature learning and training dynamics in asymptotically wide networks.