Before revision:  Furthermore, sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics . The projection methodology also clarifies the roles of the encoder and the decoder components of the network. We show that the projection of encoder hidden states onto the low dimensional space provides an initializing trajectory directing the sequence to the cluster which corresponds to that particular type of distinct dynamics and the projection of the decoder hidden states constitutes the embedded cluster attractor. Inspection of the low dimensional space and the projections onto it during training shows that the estimation of clusters separability in the embedding can be utilized to estimate the optimality of model training. We test and demonstrate our proposed interpretability methodology on synthetic examples (dynamics on a circle and an ellipse) and on 3D human body movement data .
After revision:  Furthermore, sequences are being clustered into well separable clusters in the embedding methodology to time-sequences of 3D human body movement data .