论文标题
运输评分攀登：使用前向KL和自适应神经运输的变异推理
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
论文作者
论文摘要
变异推理通常从近似分布q到后p的“反向” Kullbeck-Leibler（kl）kl（q || p）。最近的工作研究“正向” KL KL（P || Q），与反向KL不同，它不会导致低估不确定性的变异近似。本文介绍了使用汉密尔顿蒙特卡洛（HMC）和新型的自适应传输图来优化KL（P || Q）的方法，该方法可优化KL（P || Q）。传输图通过充当潜在变量空间和扭曲空间之间变量的变化来改善HMC的轨迹。 TSC使用HMC样品在优化KL时动态训练传输图（P || Q）。 TSC利用协同作用，在该协同作用下，更好的传输地图会导致更好的HMC采样，从而导致更好的传输地图。我们证明了TSC关于合成和真实数据。我们发现，在大规模数据上训练变异自动编码器时，TSC可以实现竞争性能。
Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL) KL(q||p) from the approximate distribution q to the posterior p. Recent work studies the "forward" KL KL(p||q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty. This paper introduces Transport Score Climbing (TSC), a method that optimizes KL(p||q) by using Hamiltonian Monte Carlo (HMC) and a novel adaptive transport map. The transport map improves the trajectory of HMC by acting as a change of variable between the latent variable space and a warped space. TSC uses HMC samples to dynamically train the transport map while optimizing KL(p||q). TSC leverages synergies, where better transport maps lead to better HMC sampling, which then leads to better transport maps. We demonstrate TSC on synthetic and real data. We find that TSC achieves competitive performance when training variational autoencoders on large-scale data.
