Before revision:  Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network's hidden units. Furthermore, sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics .
After revision:  Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq model trained on sequences prediction URLanized in clusters capturing similarities and differences in the dynamics of these sequences. Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation, clustering of dynamic activity, self-supervised classification, action recognition, failure prediction, etc. We test and demonstrate the application of the low dimensional space each of which corresponds to a different type of dynamics .