Research ideas

REM-Calibrated Multi-Branch Initialization

Use the paper's inverse-temperature parameter to initialize networks containing m parallel depth-N branches. Choose branch count, depth, or an explicit aggregation scale so that beta = sqrt(2(N-1)/(n log m)) stays below the critical value sqrt(2), preventing the largest random branch from dominating the aggregate. This is applicable to residual multi-branch MLPs and other architectures whose block Jacobian is a sum of products.