I'm just starting to learn about Restricted Boltzmann Machines (RBMs). So far I understand that they try to predict/output the inputs that are fed to them. If the RBM is trained on input where each record is a vector, then the RBM should likewise learn to output a vector.
While reading about RBMs, I keep coming across references to an "energy function", which I don't really understand, but my guess is that it represents the formula of a trained RBM. While reading through Geoffrey Hinton's tutorial I came across the following:
What confuses me about this paragraph is that it seems to suggest that the energy function outputs a scalar not a vector, which is inconsistent with my understanding above. Does it not actually output a scalar? Does an energy function not actually represent the architecture of a trained RBM and the means of calculating an output/prediction? Also, the article goes on to state that the variables a and b correspond to biases. If they're actually biases, why are they being multiplied with the vectors v and h? Aren't biases always supposed to be added only?