论文标题
通过预测自回归模型进行预测抽样
Predictive Sampling with Forecasting Autoregressive Models
论文作者
论文摘要
自回旋模型（ARM）当前在基于可能性的图像和音频数据建模中保持最先进的性能。通常，基于神经网络的手臂旨在快速推断，但是这些模型中的采样在不切实际的速度较慢。在本文中，我们介绍了预测采样算法：利用武器快速推理特性以加快采样的速度，同时保持模型完整的过程。我们提出了两种预测抽样的变体，即用手臂固定点迭代进行采样，并学到了预测模块。在两种情况下证明了它们的有效性：i）对二进制MNIST，SVHN和CIFAR10和II）在经过SVHN，CIFAR10和ImagEnet32的自动编码器中进行离散的潜在建模。从经验上讲，我们在ARM推理呼叫和采样速度方面表现出对基准的大量改进。
Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data. Generally, neural network based ARMs are designed to allow fast inference, but sampling from these models is impractically slow. In this paper, we introduce the predictive sampling algorithm: a procedure that exploits the fast inference property of ARMs in order to speed up sampling, while keeping the model intact. We propose two variations of predictive sampling, namely sampling with ARM fixed-point iteration and learned forecasting modules. Their effectiveness is demonstrated in two settings: i) explicit likelihood modeling on binary MNIST, SVHN and CIFAR10, and ii) discrete latent modeling in an autoencoder trained on SVHN, CIFAR10 and Imagenet32. Empirically, we show considerable improvements over baselines in number of ARM inference calls and sampling speed.
