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20212026
most citedBayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent

3 citations · 13 across the 28 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series

Gabor Szucs, Samuel Jacsev, Marcell Nemeth +2

Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must cope with distribution shifts over tim…

cs.LG2025

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…

cs.LG2025★ 3 cited

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…

cs.LG2024

Continual Learning for Behavior-based Driver Identification

Mattia Fanan, Davide Dalle Pezze, Emad Efatinasab +3

Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle thef…

cs.LG2024★ 3 cited

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…

cs.LG2022★ 1 cited

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…

cs.LG2021

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

David Dandolo, Chiara Masiero, Mattia Carletti +2

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the…