The input to the Influence Generator is the r×n dimensional vector of topic distributions from previous r timestamps of a related text stream, i.e., θ T −r:T −1 (assuming T as the current timestamp).
influence Generator essentially maps a r × n dimensional topic vector to a K dimensional influence vector.