RBMs using exponential activation functions can represent and learn data structures with strong higher-order interactions better than linear, step or ReLU activations, but only inside an analytically determined parameter window.
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Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
RBMs using exponential activation functions can represent and learn data structures with strong higher-order interactions better than linear, step or ReLU activations, but only inside an analytically determined parameter window.