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Featured Co-authors

Alessandro Achille
46 publications
Pratik Chaudhari
38 publications
Ruggero Carli
15 publications
Sean McLoone
12 publications
Alan Mishler
11 publications
Alberto Dalla Libera
11 publications
Gianmaria Silvello
10 publications
Federico Zocco
9 publications
Matteo Terzi
8 publications
Giorgio Maria Di Nunzio
5 publications
Alessandro Fabris
5 publications

∙ 01/03/2023

Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)

Process monitoring and control are essential in modern industries for en...

0 Natalie Gentner, et al. ∙

∙ 12/30/2022

Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters

An Anomaly Detection (AD) System for Self-diagnosis has been developed f...

0 Tommaso Barbariol, et al. ∙

∙ 12/21/2022

Continual Learning Approaches for Anomaly Detection

Anomaly Detection is a relevant problem that arises in numerous real-wor...

0 Davide Dalle Pezze, et al. ∙

∙ 12/04/2022

Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery

The monitoring of rotating machinery has now become a fundamental activi...

0 Lucas Costa Brito, et al. ∙

∙ 10/06/2022

Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data

Artificial Intelligence (AI) is one of the approaches that has been prop...

0 Lucas Costa Brito, et al. ∙

∙ 08/08/2022

A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring

Continual Learning aims to learn from a stream of tasks, being able to r...

0 Davide Dalle Pezze, et al. ∙

∙ 07/08/2022

Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems

The detection of anomalous behaviours is an emerging need in many applic...

0 Elisa Marcelli, et al. ∙

∙ 03/17/2022

On the Properties of Adversarially-Trained CNNs

Adversarial Training has proved to be an effective training paradigm to ...

0 Mattia Carletti, et al. ∙

∙ 02/13/2022

Learning to Rank from Relevance Judgments Distributions

Learning to Rank (LETOR) algorithms are usually trained on annotated cor...

0 Alberto Purpura, et al. ∙

∙ 02/03/2022

Algorithmic Fairness Datasets: the Story so Far

Data-driven algorithms are being studied and deployed in diverse domains...

0 Alessandro Fabris, et al. ∙

∙ 12/23/2021

AcME – Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box

In the context of human-in-the-loop Machine Learning applications, like ...

0 David Dandolo, et al. ∙

∙ 12/21/2021

Improving Robustness with Image Filtering

Adversarial robustness is one of the most challenging problems in Deep L...

8 Matteo Terzi, et al. ∙

∙ 11/30/2021

TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios

Unsupervised anomaly detection tackles the problem of finding anomalies ...

0 Tommaso Barbariol, et al. ∙

∙ 10/29/2021

Incentives for Item Duplication under Fair Ranking Policies

Ranking is a fundamental operation in information access systems, to fil...

0 Giorgio Maria Di Nunzio, et al. ∙

∙ 05/21/2021

Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing

We conduct an audit of pricing algorithms employed by companies in the I...

0 Alessandro Fabris, et al. ∙

∙ 03/03/2021

Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study

Dimensionality reduction is a important step in the development of scala...

15 Federico Zocco, et al. ∙

∙ 02/23/2021

An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery

The monitoring of rotating machinery is an essential task in today's pro...

0 Lucas Costa Brito, et al. ∙

∙ 02/22/2021

Neural Feature Selection for Learning to Rank

LEarning TO Rank (LETOR) is a research area in the field of Information ...

0 Alberto Purpura, et al. ∙

∙ 09/02/2020

Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms

Search Engines (SE) have been shown to perpetuate well-known gender ster...

0 Alessandro Fabris, et al. ∙

∙ 08/20/2020

β-Variational Classifiers Under Attack

Deep Neural networks have gained lots of attention in recent years thank...

0 Marco Maggipinto, et al. ∙

∙ 08/03/2020

IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces

Learning useful representations of complex data has been the subject of ...

7 Marco Maggipinto, et al. ∙

∙ 08/03/2020

Proximal Deterministic Policy Gradient

This paper introduces two simple techniques to improve off-policy Reinfo...

0 Marco Maggipinto, et al. ∙

∙ 07/22/2020

Adversarial Training Reduces Information and Improves Transferability

Recent results show that features of adversarially trained networks for ...

0 Matteo Terzi, et al. ∙

∙ 07/21/2020

Interpretable Anomaly Detection with DIFFI: Depth-based Feature Importance for the Isolation Forest

Anomaly Detection is one of the most important tasks in unsupervised lea...

0 Mattia Carletti, et al. ∙

∙ 10/08/2019

Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications

In many real-world applications of Machine Learning it is of paramount i...

5 Matteo Terzi, et al. ∙

∙ 03/11/2019

Robot kinematic structure classification from time series of visual data

In this paper we present a novel algorithm to solve the robot kinematic ...

0 Alberto Dalla Libera, et al. ∙

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