  Following the work of arXiv:2101.09512, we are interested in clustering a
given multi-variate series in an unsupervised manner. We would like to segment
and cluster the series such that the resulting blocks present in each cluster
are coherent with respect to a predefined model structure (e.g. a physics model
with a functional form defined by a number of parameters). However, such
approach might have its limitation, partly because there may exist multiple
models that describe the same data, and partly because the exact model behind
the data may not immediately known. Hence, it is useful to establish a general
framework that enables the integration of plausible models and also
accommodates data-driven approach into one approximated model to assist the
clustering task. Hence, in this work, we investigate the use of neural
processes to build the approximated model while yielding the same assumptions
required by the algorithm presented in arXiv:2101.09512.
