From: approximateinference.org/AABI/2022/Symposium/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: (The following review is provided by one of the program chairs.)

This paper considers learning a prior distribution of a latent variable model via meta-learning. The prior is parameterised in a similar fashion as VampPrior, which is a mixture of Gaussian with the mean and variance of each component parameterised by a neural network transformation of some "pseudo-inputs". The main difference here is (1) the prior neural network is different from the encoder network used for $q(z|x)$, and (2) in few-shot learning scenario the pseudo inputs and the network for the prior is learned in a MAML fashion.

This work concerns about prior design for probabilistic models which is relevant to the AABI community. Experimental results are encouraging, but the advantage of the approach is not fully cleared yet. Especially, I am not sure if for the few-shot learning experiments, the parameters of other baselines (MoG with direct mean&variance parameterisation and VampPrior) are also meta-learned via MAML.