Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their flow is a neural network (NN) whose parameters are predicted by another NN. The latter grows quadratically with the size of the former and thus an efficient technique for parametrization is needed. We propose block neural autoregressive flow (B-NAF), a much more compact universal approximator of density functions, where we model a bijection directly using a single feed-forward network. Invertibility is ensured by carefully designing each affine transformation with block matrices that make the flow autoregressive and (strictly) monotone. We compare B-NAF to NAF and other established flows on density estimation and approximate inference for latent variable models. Our proposed flow is competitive across datasets while using orders of magnitude fewer parameters.

本文介绍了一种新型的神经网络模型叫Block neural autoregressive flow（B-NAF），通过使用单个前馈网络直接模拟双密度函数的变换关系实现了紧凑型的通用密度函数的拟合。同时，本文还比较了B-NAF和其他流行的自动编码器，发现B-NAF在保证了准确性的同时，使用的参数数量比这些流行的自动编码器更少。

块状神经自回归流