Predicting future states or actions of a given system remains a fundamental,
yet unsolved challenge of intelligence, especially in the scope of complex and
non-deterministic scenarios, such as modeling behavior of humans. Existing
approaches provide results under strong assumptions concerning unimodality of
future states, or, at best, assuming specific probability distributions that
often poorly fit to real-life conditions. In this work we introduce a robust
and flexible probabilistic framework that allows to model future predictions
with virtually no constrains regarding the modality or underlying probability
distribution. To achieve this goal, we leverage a hypernetwork architecture and
train a continuous normalizing flow model. The resulting method dubbed RegFlow
achieves state-of-the-art results on several benchmark datasets, outperforming
competing approaches by a significant margin.

本文提出了一种弹性且鲁棒的概率框架 RegFlow，采用超网络架构和连续正常化流模型进行训练，无需对未来状态的单峰性或概率分布做出任何限制，成果在多项基准数据集上表现优于竞争方法。