Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single neurons. In this work, we study the relationship between the statistical properties of the weights and the nonlinearity of the hidden unit in Restricted Boltzmann Machines (RBMs) on the one side, and the distribution induced on binary visible units. We do this for four commonly used activation functions: Linear, Step, ReLU, and Exponential, and make qualitative predictions about the ability of these models to learn distributions with strong higher order interactions over the visible nodes. We show that in g...
| Authors | Giovanni di Sarra, Yasser Roudi |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 18 May 2026 |
| Last updated at source | 23 June 2026 |
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