Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Authorship AttributionSimilar Papers 제목 키워드 기반
Variational Autoencoder with Embedded Student-$t$ Mixture Model for Authorship Attribution
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 t…
Authorship AttributionGeneral ClassificationThe advantage of using Student's t-priors in variational autoencoders
Is it optimal to use the standard Gaussian prior in variational autoencoders? With Gaussian distributions, which are not weakly informative priors, variational autoencoders struggle to reconstruct the actual data. We pro…
Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI
Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement, where models learn spuri…
Representation LearningContrastive LearningFew-Shot LearningText DetectionImproving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
We consider the task of estimating variational autoencoders (VAEs) when the training data is incomplete. We show that missing data increases the complexity of the model's posterior distribution over the latent variables …
ImputationInfinite Variational Autoencoder for Semi-Supervised Learning
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowin…