论文标题
可扩展的归一流流量的排列不变密度
Scalable Normalizing Flows for Permutation Invariant Densities
论文作者
论文摘要
建模集是机器学习中的一个重要问题，因为可以在许多域中找到这种类型的数据。一种有前途的方法定义了一个置换不变密度的家族，并具有连续的归一化流量。这使我们能够直接最大程度地提高可能性，并轻松地采样新的实现。在这项工作中，我们演示了计算痕迹是如何在训练和推理期间出现的问题，从而限制了其实用性。我们提出了一种定义置换量表转换的替代方法，该转换给出了封闭形式的痕迹。这不仅导致训练时的进步，还可以提高最终表现。我们证明了我们方法对点过程和一般集合建模的好处。
Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densities with continuous normalizing flows. This allows us to maximize the likelihood directly and sample new realizations with ease. In this work, we demonstrate how calculating the trace, a crucial step in this method, raises issues that occur both during training and inference, limiting its practicality. We propose an alternative way of defining permutation equivariant transformations that give closed form trace. This leads not only to improvements while training, but also to better final performance. We demonstrate the benefits of our approach on point processes and general set modeling.
