Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

Abstract

Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts. In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task. Variational autoencoders (VAEs) have had tremendous success in learning latent representations. However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space. In this work, we are extending a VAE with an embedded Gaussian mixture model to a Student-t mixture model, which allows for an independent control of the "heaviness" of the respective tails of the implied probability densities. Experiments over an Amazon review dataset indicate superior performance of the proposed method.This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/.1 Each review text had a length of less than 1000 tokens, i.e. relatively short text sizes.

Topographic VAEs learn Equivariant Capsules

“…Our work assumes a similar generative model to these works while additionally allowing for efficient estimation of the model through variational inference [32,46]. Although our work is not the first to combine Student's-t distributions and variational inference [7], it is the first to provide an efficient method to do so for Topographic Student's-t distributions.…”