Features of both words and sentences will be aggregated to enhance the representation ability.

Existing FER methods typically report overall accuracy on highly imbalanced test sets but exhibit low performance in terms of the mean accuracy across all expression classes.

Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

We use the discrete variablezd,t to represent the topic that thetth word is generated from.

Bayesian model evidence gives a clear criteria for such model selection. However, computing model evidence requires integration over the likelihood, which is challenging, particularly when the likelihood is non-closed-form and/or expensive.