The recurrent layer learns a set of hidden states corresponding to different time steps, and the max pooling layer extracts the most salient information from the hidden states.
the assumptions become more problematic if we increase the window size m. We propose to address the limitations by employing RNNs to realize convolution filters, which we term recurrent neural filters (RNFs).