19 citations · 35 across the 9 of their papers we have counts for
5 papers · 1 filter
Novel Applications for VAE-based Anomaly Detection Systems
Luca Bergamin, Tommaso Carraro, Mirko Polato +1
The recent rise in deep learning technologies fueled innovation and boosted scientific research. Their achievements enabled new research directions for deep generative modeling (DG…
Bayes Point Rule Set Learning
Fabio Aiolli, Luca Bergamin, Tommaso Carraro +1
Interpretability is having an increasingly important role in the design of machine learning algorithms. However, interpretable methods tend to be less accurate than their black-box…
Conditioned Variational Autoencoder for top-N item recommendation
Tommaso Carraro, Mirko Polato, Fabio Aiolli
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The pr…
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
Mirko Polato, Fabio Aiolli
A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference lea…
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato +1
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in a…