Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines

ArXi:2605.19178v1 Announce Type: cross The great success of neural networks in recognizing hidden patterns and correlations in complex data lies in the way they take advantage of the large number of parameters and nonlinear single-unit activation, jointly. Restricted Boltzmann Machines (RBMs) provide a simple yet powerful framework for studying the impact of activation nonlinearities on performance and representation.