164 citations · 413 across the 24 of their papers we have counts for
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stat.ML2018
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series
Daniel J. Trosten, Andreas S. Strauman, Michael Kampffmeyer +1
The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths. If t…
stat.ML2018
The Deep Kernelized Autoencoder
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +2
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of…