论文标题
准自动进取残差（QUOR）流动
Quasi-Autoregressive Residual (QuAR) Flows
论文作者
论文摘要
归一化流是从这些分布的样本中学习和建模概率分布的强大技术。当前的最新结果是基于残留流的，因为这些可以比耦合层建模更大的假设空间。但是，剩余流量在训练和使用上都非常昂贵，这在实践中限制了其适用性。在本文中，我们使用准自动回旋（QUAR）方法简化了残差流。与标准剩余流量方法相比，这种简化保留了残留流的许多好处，同时大大减少了计算时间和内存需求，从而使基于流动的建模方法更加可触及并扩大了其潜在的适用性。
Normalizing Flows are a powerful technique for learning and modeling probability distributions given samples from those distributions. The current state of the art results are built upon residual flows as these can model a larger hypothesis space than coupling layers. However, residual flows are extremely computationally expensive both to train and to use, which limits their applicability in practice. In this paper, we introduce a simplification to residual flows using a Quasi-Autoregressive (QuAR) approach. Compared to the standard residual flow approach, this simplification retains many of the benefits of residual flows while dramatically reducing the compute time and memory requirements, thus making flow-based modeling approaches far more tractable and broadening their potential applicability.
