Adaptive Symmetry

Adaptive symmetry, a concept contrasting with traditional conserved symmetries in physics, explores how systems, particularly deep neural networks (DNNs), develop complex structures by breaking initially symmetric states in response to external signals. Current research focuses on using statistical mechanics models, often incorporating probabilistic graphical models and circuit-based representations, to understand this symmetry-breaking process during DNN training. This framework offers a novel perspective on the optimization dynamics of complex systems, potentially leading to improved understanding of learning mechanisms and the design of more efficient and robust artificial intelligence.