论文标题
FRMDN：基于流动的复发混合物密度网络
FRMDN: Flow-based Recurrent Mixture Density Network
论文作者
论文摘要
一类复发混合物密度网络是一类重要的概率模型，以序列建模和序列到序列映射应用程序进行了广泛使用。在这类模型中，每个时间步中的目标序列的密度是由带有复发神经网络给出的参数的高斯混合模型模拟的。在本文中，我们通过在每个时间步长的非线性转换目标序列上定义高斯混合模型来概括复发混合物密度网络。非线性转化的空间是通过标准化流量而产生的。我们观察到，该模型可显着改善通过对数似然度测得的图像序列的拟合。我们还将所提出的模型应用于某些语音和图像数据，并观察到该模型具有重要的建模功率，从而超过了其他最新方法，从而超过了对数可能性。
The class of recurrent mixture density networks is an important class of probabilistic models used extensively in sequence modeling and sequence-to-sequence mapping applications. In this class of models, the density of a target sequence in each time-step is modeled by a Gaussian mixture model with the parameters given by a recurrent neural network. In this paper, we generalize recurrent mixture density networks by defining a Gaussian mixture model on a non-linearly transformed target sequence in each time-step. The non-linearly transformed space is created by normalizing flow. We observed that this model significantly improves the fit to image sequences measured by the log-likelihood. We also applied the proposed model on some speech and image data, and observed that the model has significant modeling power outperforming other state-of-the-art methods in terms of the log-likelihood.
