14 citations · 41 across the 37 of their papers we have counts for
44 papers
Towards Autonomous Commissioning of Industrial Drives via Multi-Objective Bayesian Optimization
David Petrovic, Gian Antonio Susto, Angelo Cenedese
The commissioning of industrial electric drives still relies heavily on manual tuning of cascaded control loops, requiring expert knowledge and significant time. In this paper, we…
Reinforcement Learning for Durable Algorithmic Recourse
Marina Ceccon, Alessandro Fabris, Goran Radanović +2
Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g.,…
A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management
Matteo Cederle, Marco Fabris, Gian Antonio Susto
This study introduces a novel multi-objective reinforcement learning (MORL) approach for autonomous intersection management, aiming to balance traffic efficiency and environmental…
Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This wor…
Testing software for non-discrimination: an updated and extended audit in the Italian car insurance domain
Marco Rondina, Antonio Vetrò, Riccardo Coppola +5
Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional req…
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A…
TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection
Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze +2
Detecting objects with visual sensors is crucial for numerous mobile robotics applications, from autonomous navigation to inspection. However, robots often need to operate under si…
Replay Consolidation with Label Propagation for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Marina Ceccon +5
Continual Learning (CL) aims to learn new data while remembering previously acquired knowledge. In contrast to CL for image classification, CL for Object Detection faces additional…
Fairness Evolution in Continual Learning for Medical Imaging
Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris +1
Deep Learning has advanced significantly in medical applications, aiding disease diagnosis in Chest X-ray images. However, expanding model capabilities with new data remains a chal…
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised se…
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
Davide Frizzo, Francesco Borsatti, Alessio Arcudi +3
Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomal…
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
Valentina Zaccaria, Chiara Masiero, David Dandolo +1
While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challeng…
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris +1
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in re…
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
Nikola Bugarin, Jovana Bugaric, Manuel Barusco +2
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes ov…
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Valentina Zaccaria, David Dandolo, Chiara Masiero +1
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel…
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera, Jacopo Andreoli, Davide Dalle Pezze +2
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has improved considerably upon traditional m…
Improving robustness of quantum feedback control with reinforcement learning
Manuel Guatto, Gian Antonio Susto, Francesco Ticozzi
Obtaining reliable state preparation protocols is a key step towards practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this wo…
Enhancing Interpretability and Generalizability in Extended Isolation Forests
Alessio Arcudi, Davide Frizzo, Chiara Masiero +1
Anomaly Detection (AD) focuses on identifying unusual behaviors in complex datasets. Machine Learning (ML) algorithms and Decision Support Systems (DSSs) provide effective solution…
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)
Natalie Gentner, Gian Antonio Susto
Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history…
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
Tommaso Barbariol, Davide Masiero, Enrico Feltresi +1
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecastin…
Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito +1
The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from r…
Continual Learning Approaches for Anomaly Detection
Davide Dalle Pezze, Eugenia Anello, Chiara Masiero +1
Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task…
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito +1
Artificial Intelligence (AI) is one of the approaches that has been proposed to analyze the collected data (e.g., vibration signals) providing a diagnosis of the asset's operating…
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Davide Dalle Pezze, Denis Deronjic, Chiara Masiero +3
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classi…