5 citations · 11 across the 38 of their papers we have counts for
21 papers · 1 filter
Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
Alessio Arcudi, Alessandro Ferreri, Francesco Borsatti +1
Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability…
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3
In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highlighting anomalous r…
Towards Fair and Efficient allocation of Mobility-on-Demand resources through a Karma Economy
Matteo Cederle, Saverio Bolognani, Gian Antonio Susto
Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due…
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze +3
Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is extended to new grid z…
Towards Continual Visual Anomaly Detection in the Medical Domain
Manuel Barusco, Francesco Borsatti, Nicola Beda +2
Visual Anomaly Detection (VAD) seeks to identify abnormal images and precisely localize the corresponding anomalous regions, relying solely on normal data during training. This app…
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
Alessio Arcudi, Davide Sartor, Alberto Sinigaglia +2
This paper introduces MANGO (Multilayer Abstraction for Nested Generation of Options), a novel hierarchical reinforcement learning framework designed to address the challenges of l…
Explainable Anomaly Detection for Electric Vehicles Charging Stations
Matteo Cederle, Andrea Mazzucco, Andrea Demartini +4
Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability…
Towards Explainable Anomaly Detection in Shared Mobility Systems
Elnur Isgandarov, Matteo Cederle, Federico Chiariotti +1
Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations,…
MoViAD: A Modular Library for Visual Anomaly Detection
Manuel Barusco, Francesco Borsatti, Arianna Stropeni +2
VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for un…
Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots
Marco Calì, Alberto Sinigaglia, Niccolò Turcato +2
Deep Reinforcement Learning (RL) has emerged as a powerful method for addressing complex control problems, particularly those involving underactuated robotic systems. However, in s…
Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond
Marina Ceccon, Giandomenico Cornacchia, Davide Dalle Pezze +2
Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legis…
A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models
Davide Frizzo, Francesco Borsatti, Gian Antonio Susto
Predictive Maintenance (PdM) is pivotal in Industry 4.0 and 5.0, proactively enhancing efficiency through accurate equipment Remaining Useful Life (RUL) prediction, thus optimizing…
Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing
Adrian Poniatowski, Natalie Gentner, Manuel Barusco +3
In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant market share in vario…
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study
Manuel Barusco, Francesco Borsatti, Youssef Ben Khalifa +2
Semiconductor manufacturing is a complex, multistage process. Automated visual inspection of Scanning Electron Microscope (SEM) images is indispensable for minimizing equipment dow…
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations
Davide Sartor, Alberto Sinigaglia, Gian Antonio Susto
Conventional techniques for imposing monotonicity in MLPs by construction involve the use of non-negative weight constraints and bounded activation functions, which pose well-known…
Simple and Effective Specialized Representations for Fair Classifiers
Alberto Sinigaglia, Davide Sartor, Marina Ceccon +1
Fair classification is a critical challenge that has gained increasing importance due to international regulations and its growing use in high-stakes decision-making settings. Exis…
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
Arianna Stropeni, Francesco Borsatti, Manuel Barusco +3
Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT)…
Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera +17
In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields…
Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Gian Antonio Susto
Real-time object detectors like YOLO achieve exceptional performance when trained on large datasets for multiple epochs. However, in real-world scenarios where data arrives increme…
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection
Manuel Barusco, Lorenzo D'Antoni, Davide Dalle Pezze +2
Visual Anomaly Detection (VAD) is a critical task in computer vision with numerous real-world applications. However, deploying these models on edge devices presents significant cha…
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these deve…