论文标题
随机归一化流
Stochastic Normalizing Flows
论文作者
论文摘要
我们介绍了随机归一化流，即连续归一化流的扩展，以使用随机微分方程（SDE）（SDES），以最大似然估计和变异推理（VI）。使用粗糙路径的理论，基本的布朗运动被视为潜在变量和近似的，从而有效地训练神经SDE作为随机神经常规微分方程。这些SDE可用于构建有效的马尔可夫链，以从给定数据集的基础分布中采样。此外，通过考虑具有规定的固定分布的靶向SDE家族，我们可以将VI应用于随机MCMC中超参数的优化。
We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs). Using the theory of rough paths, the underlying Brownian motion is treated as a latent variable and approximated, enabling efficient training of neural SDEs as random neural ordinary differential equations. These SDEs can be used for constructing efficient Markov chains to sample from the underlying distribution of a given dataset. Furthermore, by considering families of targeted SDEs with prescribed stationary distribution, we can apply VI to the optimization of hyperparameters in stochastic MCMC.
