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%24 runtime and %24O(n^2)%24 space complexity. In this paper%2C we introduce a method that has %24O(n^2)%24 runtime and %24O(n)%24 space complexity%2C without assuming independence.%0A We validate our model on both synthetic and real-world datasets%2C showing that our method's accuracy is comparable to that of prior work We demonstrate that our approach can be used on unprecedentedly large datasets%2C such as a real-world 1%2C000%2C000-cell scRNA-seq dataset%3B this was impossible with previous approaches. Our method maintains the flexibility of prior work%2C such as the ability to handle multi-modal tensor-variate datasets and the ability to work with data of arbitrary marginal distributions. An additional advantage of our method is that%2C unlike prior work%2C our hyperparameters are easily interpretable.?quality=80&w=800)
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