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A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering
TLDR
An intuitive interpretation of the inner representation of a conditioned variational autoencoder (C-VAE) for collaborative filtering and shows that in the latent space conditions on correlated genres map users in close clusters enables the model to be used for profiling purposes.
Conditioned Variational Autoencoder for top-N item recommendation
TLDR
A Conditioned Variational Autoencoder for constrained top-N item recommendation where the recommended items must satisfy a given condition is proposed, and it is suggested that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation.
Novel Applications for VAE-based Anomaly Detection Systems
TLDR
A novel model that repurposes and extends the Auto-encoding Binary Classifier (ABC) anomaly detector, using the Variational Autoencoder (VAE) and survey the limitations of existing approaches and explore many tools to show the model’s inner workings in an interpretable way.
Bayes Point Rule Set Learning
TLDR
An effective bottom-up extension of the popular FIND-S algorithm to learn DNF-type rulesets, approximating the Bayes Optimal Classifier by aggregating DNF decision rules.