2 citations · 4 across the 5 of their papers we have counts for
5 papers
Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Luca Bergamin, Giovanna Maria Dimitri, Fabio Aiolli
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this fiel…
(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency…
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…