Partitioning a set of elements into subsets of a priori unknown sizes is
essential in many applications. These subset sizes are rarely explicitly
learned - be it the cluster sizes in clustering applications or the number of
shared versus independent generative latent factors in weakly-supervised
learning. Probability distributions over correct combinations of subset sizes
are non-differentiable due to hard constraints, which prohibit gradient-based
optimization. In this work, we propose the differentiable hypergeometric
distribution. The hypergeometric distribution models the probability of
different group sizes based on their relative importance. We introduce
reparameterizable gradients to learn the importance between groups and
highlight the advantage of explicitly learning the size of subsets in two
typical applications: weakly-supervised learning and clustering. In both
applications, we outperform previous approaches, which rely on suboptimal
heuristics to model the unknown size of groups.

本文提出可微分超几何分布，并利用其优势来显式地学习子集的大小，在弱监督学习和聚类两个应用中超越依赖于次优启发式模型集群大小未知的传统方法。