In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable to process this kind of representations, since sets may vary in cardinality and elements lack a meaningful ordering. In this paper, we present a new neural network architecture, called RepSet, that can handle examples that are represented as sets of vectors. The proposed model computes the correspondences between an input set and some hidden sets by solving a series of network flow problems. This representation is then fed to a standard neural network architecture to produce the output. The architecture allows end-to-end gradient-based learning. We demonstrate RepSet on classification tasks, including text categorization, and graph classification, and we show that the proposed neural network achieves performance better or comparable to state-of-the-art algorithms.

本文提出了一种名为RepSet的新型神经网络体系结构，通过解决一系列网络流问题来计算输入集与一些隐藏集之间的对应关系。该体系结构可以处理表示为向量集合的示例，并且可以在端到端的梯度优化学习中使用，在文本分类和图分类等任务中取得了优秀的性能。

用神经网络学习集合表示