14 citations · 41 across the 37 of their papers we have counts for
6 papers · 1 filter
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Learning useful representations of complex data has been the subject of extensive research for many years. With the diffusion of Deep Neural Networks, Variational Autoencoders have…
Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms
Alessandro Fabris, Alberto Purpura, Gianmaria Silvello +1
Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found en…
-Variational Classifiers Under Attack
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision. However, despite their popularity, it has b…
Proximal Deterministic Policy Gradient
Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off-policy RL as a stochastic proximal point iteration…
Adversarial Training Reduces Information and Improves Transferability
Matteo Terzi, Alessandro Achille, Marco Maggipinto +1
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter p…
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviours with respect to historical data. In particular, multivariate Anomaly Detection has an imp…