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.

中文翻译：

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RegFlow：基于概率流的回归以进行未来预测

预测给定系统的未来状态或动作仍然是智能的一项基本但尚未解决的挑战，特别是在复杂且不确定的情况下，例如对人类行为建模。现有方法在有关未来状态的单峰性的强大假设下提供了结果，或者最多只能假设通常不太适合现实生活条件的特定概率分布。在这项工作中，我们引入了一个健壮而灵活的概率框架，该框架可以对未来的预测进行建模，而对模式或潜在的概率分布几乎没有任何限制。为了实现此目标，我们利用超网络体系结构并训练连续的标准化流模型。所得的称为RegFlow的方法可在多个基准数据集上获得最新的结果，