Parameters

The network parameters, to be optimized/learned:

1. \( \mathbf{a} \) represents the visible bias, a vector of same length as \( \mathbf{x} \).
2. \( \mathbf{b} \) represents the hidden bias, a vector of same lenght as \( \mathbf{h} \).
3. \( W \) represents the interaction weights, a matrix of size \( M\times N \).

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