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CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis. (arXiv:2204.08988v1 [cs.LG])
April 20, 2022, 1:12 a.m. | Or Dinari, Raz Zamir, John W. Fisher III, Oren Freifeld
cs.LG updates on arXiv.org arxiv.org
In the realm of unsupervised learning, Bayesian nonparametric mixture models,
exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled
approach for adapting the complexity of the model to the data. Such models are
particularly useful in clustering tasks where the number of clusters is
unknown. Despite their potential and mathematical elegance, however, DPMMs have
yet to become a mainstream tool widely adopted by practitioners. This is
arguably due to a misconception that these models scale poorly as well …
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