论文标题
用动态归一化流量建模连续的随机过程
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
论文作者
论文摘要
标准化流量将简单的基本分布转变为复杂的目标分布，并已证明是数据生成和密度估计的强大模型。在这项工作中，我们提出了一种由维纳过程的差异变形驱动的一种新型的归一化流动。结果，我们获得了一个丰富的时间序列模型，其可观察的过程继承了其基本过程的许多吸引人特性，例如对似然和边缘的有效计算。此外，我们的连续处理为不规则时间序列提供了一个自然的框架，并具有独立的到达过程，包括直接插值。我们说明了在流行随机过程中提出的模型的理想特性，并在一系列有关合成和现实世界数据的实验中证明了其对变异RNN和潜在颂歌基线的卓越灵活性。
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.
