论文标题
隐式模型的神经近似统计数据
Neural Approximate Sufficient Statistics for Implicit Models
论文作者
论文摘要
我们考虑了如何自动构建隐式生成模型的摘要统计信息的基本问题，在这种模型中，对可能性函数的评估是棘手的，但是可以从模型中抽样数据。这个想法是将构建足够统计数据的任务构建为在深层神经网络的帮助下学习最大化数据表示的相互信息。 Infomax学习程序无需估计任何密度或密度比。我们将我们的方法应用于传统的近似贝叶斯计算和最近的神经可能性方法，从而提高了它们在一系列任务上的表现。
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.
