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We propose a VAE for Transformers by developing a variational informationbottleneck regulariser for Transformer embeddings. We formalise the embeddingspace of Transformer encoders as mixture probability distributions, and useBayesian nonparametrics to derive a nonparametric variational informationbottleneck (NVIB) for such attention-based embeddings. The variable number ofmixture components supported by nonparametric methods captures the variablenumber of vectors supported by attention, and the exchangeability of ournonparametric distributions captures the permutation invariance of attention.This allows NVIB to regularise the number of vectors accessible with attention,as well as the amount of information in individual vectors. By regularising thecross-attention of a Transformer encoder-decoder with NVIB, we propose anonparametric variational autoencoder (NVAE). Initial experiments on training aNVAE on natural language text show that the induced embedding space has thedesired properties of a VAE for Transformers.

A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck