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.

翻译：在arXiv:2101.09512的工作之后,我们有兴趣以不受监督的方式将某个多变量系列分组,我们希望对系列进行分解和分组,使每个组群中产生的区块与预先定义的模式结构(例如物理学模型,其功能形式由若干参数界定)保持一致。然而,这种方法可能会有其局限性,部分原因是可能存在描述相同数据的多种模型,部分原因是数据背后的确切模型可能并不立即为人所知。因此,有必要建立一个总框架,使可信的模型能够集成一体,并纳入数据驱动方法,形成一种大致的模型,以协助集群任务。因此,在这项工作中,我们调查使用神经过程来构建近似模型的情况,同时得出ARXiv:2101.09512的算法所要求的相同假设。