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Apprentissage par cœur
Publications associées (39)
GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification
Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...
Amsterdam2024Optimizing Dynamic Aperture Studies with Active Learning
Davide Di Croce, Tatiana Pieloni, Ekaterina Krymova, Massimo Giovannozzi
Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...
2024Work in progress: Imagining / Designing informal spaces for learning.
The paper presents ICAP (interactive, constructive, active and passive) as the theoretical framework to understand the role of informal learning spaces as an active learning tool when students have informal meetings to work on projects. Students in our En ...
Aalborg Universitetsforlag2023Action Levers towards Sustainable Wellbeing: Re-Thinking Negative Emissions, Sufficiency, Deliberative Democracy
Systems theory defines leverage points as places to intervene in order to change a system. Points with high impact on system behavior are notoriously hard to act upon, and indeed most policy intervention is based at the lowest level (#12 in Donella Meadows ...
EPFL2023A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering
François Maréchal, Julia Granacher
In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...
Oxford2023Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different trad ...
NATURE PORTFOLIO2022Active Learning for Imbalanced Civil Infrastructure Data
Diego Matteo Antognini, Adelmo Cristiano Innocenza Malossi, Ioana Giurgiu
Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the pr ...
2022Active learning for multi-objective optimization of processes and energy systems
François Maréchal, Julia Granacher
In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not ...
Elsevier Science2022Active Learning of Bayesian Probabilistic Movement Primitives
Jean-Marc Odobez, Sylvain Calinon, Hakan Girgin, Thibaut Antoine Kulak
Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distribu ...
IEEE2021