In general, an FFNN consists of an input layer with activation a (0) := emo s (w i ) ∈ R |s| followed by multiple hidden layers with activation a (l+1) := σ(W (l+1) a (l) + b (l+1) ) where W (l+1) and b (l+1) are the weights and biases for layer l + 1 and σ is a nonlinear activation function.
as can be seen, regarding VaD, Valence is by far the most important dimension with a performance drop of .12 when ablating it.