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- W2904054107 abstract "We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learning, and nominal and ordinal classification, with the model. We analyze generative properties of the approach, and the classification effectiveness under nominal and ordinal classification, using two benchmark datasets. Our results show that our model can achieve comparable results with relevant baselines in both of the classification tasks." @default.
- W2904054107 created "2018-12-22" @default.
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- W2904054107 date "2018-12-18" @default.
- W2904054107 modified "2023-09-27" @default.
- W2904054107 title "A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression" @default.
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