论文标题
优化退火重要性抽样超参数
Optimization of Annealed Importance Sampling Hyperparameters
论文作者
论文摘要
退火重要性采样（AIS）是一种流行的算法，用于估计深层生成模型的棘手边际可能性。尽管AIS保证为任何一组超参数提供无偏见的估计，但共同的实现依赖于简单的启发式方法，例如在计算预算有限时会影响估算性能的初始和目标分布之间的几何平均桥接分布。为了减少采样迭代的数量，我们提出了一个参数AIS过程，其柔性中间分布由残余密度相对于几何均值路径定义。我们的方法允许在退火分布，固定线性计划在潜在变量模型中选择超参数选择的参数共享。我们评估了优化路径AIS的性能，以对深度生成模型的边际可能性估计，并将其比较它与更多计算密集的AIS进行比较。
Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between initial and the target distribution which affect the estimation performance when the computation budget is limited. In order to reduce the number of sampling iterations, we present a parameteric AIS process with flexible intermediary distributions defined by a residual density with respect to the geometric mean path. Our method allows parameter sharing between annealing distributions, the use of fix linear schedule for discretization and amortization of hyperparameter selection in latent variable models. We assess the performance of Optimized-Path AIS for marginal likelihood estimation of deep generative models and compare it to compare it to more computationally intensive AIS.
