论文标题
使用可区分的高几何分布来学习组重要性
Learning Group Importance using the Differentiable Hypergeometric Distribution
论文作者
论文摘要
在许多应用中，将一组元素分配到先验未知大小的子集中是必不可少的。这些子集大小很少被明确学习 - 无论是聚类应用程序中的群集大小还是共享的人数与独立生成的潜在因素的数量。由于硬限制禁止基于梯度的优化，因此无法差异的子集大小组合的概率分布是不可差异的。在这项工作中，我们提出了可区分的超几何分布。超几何分布基于不同组大小的相对重要性的概率模型。我们介绍了可重新分配的梯度，以了解组之间的重要性，并强调在两个典型应用程序中明确学习子集的大小的优势：弱监督的学习和聚类。在这两个应用程序中，我们都胜过以前的方法，这些方法依靠次优启发式方法来对群体的大小进行建模。
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.
