Reviews: Learning Representations for Time Series Clustering

The submission proposes a model for time-series clustering. The model is a novel combination of several existing components: a) a deep recurrent auto-encoder using dilated RNNs, b) a spectral relaxation of the K-means objective and c) a self-supervision loss to discriminate time-series corrupted by random shuffling from the original ones. The model is evaluated on a common benchmark for time-series clustering and achieves superior performance to existing methods. Overall I feel positive about the proposed method as the quantitative results look promising and using the spectral relaxation of K-means for deep clustering is novel and original. Nevertheless I do have some concerns about the submission in its current form: 1.)