论文标题
朝着复发的自回旋流程模型
Towards Recurrent Autoregressive Flow Models
论文作者
论文摘要
非平稳分布产生的随机过程很难用常规模型（例如高斯过程）表示。这项工作介绍了复发性自回归流，作为通过标准化流量进行通用随机过程建模的方法。所提出的方法通过调节归一流的流动的参数，通过复发性神经连接来定义一个顺序过程中每个变量的条件分布。复杂的条件关系是通过经常性网络参数学习的。在这项工作中，我们提出了一个反复流动池的初始设计，以及一种训练模型以匹配观察到的经验分布的方法。我们通过一系列实验证明了这类模型的有效性，其中模型在三个复杂的随机过程上进行了训练。我们强调了当前配方的缺点，并提出了一些潜在的解决方案。
Stochastic processes generated by non-stationary distributions are difficult to represent with conventional models such as Gaussian processes. This work presents Recurrent Autoregressive Flows as a method toward general stochastic process modeling with normalizing flows. The proposed method defines a conditional distribution for each variable in a sequential process by conditioning the parameters of a normalizing flow with recurrent neural connections. Complex conditional relationships are learned through the recurrent network parameters. In this work, we present an initial design for a recurrent flow cell and a method to train the model to match observed empirical distributions. We demonstrate the effectiveness of this class of models through a series of experiments in which models are trained on three complex stochastic processes. We highlight the shortcomings of our current formulation and suggest some potential solutions.
