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VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

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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 ...

Amsterdam2024

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer2024

Work in progress: Imagining / Designing informal spaces for learning.

Ingrid Le Duc

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 Universitetsforlag2023

Divergent X-ray tomography reconstruction and optimisation

Youssef Haouchat

In diverse fields such as medical imaging, astrophysics, geophysics, or material study, a common challenge exists: reconstructing the internal volume of an object using only physical measurements taken from its exterior or surface. This scientific approach ...

2023

Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity

Alexander Mathis

Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator (top-down approach), or localize all body parts first and then link them to ...

2023

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 PORTFOLIO2022

Bridging the gap between model-driven and data-driven methods in the era of Big Data

Gael Lederrey

Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...

EPFL2022

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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 ...

2022

Active 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

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE2020