1 citations · 2 across the 6 of their papers we have counts for
7 papers
Disentangling Model and Human Data Uncertainty in Apparent Facial Age Estimation
Andrei Foitos, Ivo Pascal de Jong, Matias Valdenegro-Toro
Estimating the apparent age of individuals from facial images is challenging due to the subjective nature of perception and the inherent variability of the data. We investigate the…
Why Large Language Models can Secretly Outperform Embedding Similarity in Information Retrieval
Matei Benescu, Ivo Pascal de Jong
With the emergence of Large Language Models (LLMs), new methods in Information Retrieval are available in which relevance is estimated directly through language understanding and r…
Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Models
Mirko Borszukovszki, Ivo Pascal de Jong, Matias Valdenegro-Toro
To leverage the full potential of Large Language Models (LLMs) it is crucial to have some information on their answers' uncertainty. This means that the model has to be able to qua…
Unified Uncertainties: Combining Input, Data and Model Uncertainty into a Single Formulation
Matias Valdenegro-Toro, Ivo Pascal de Jong, Marco Zullich
Modelling uncertainty in Machine Learning models is essential for achieving safe and reliable predictions. Most research on uncertainty focuses on output uncertainty (predictions),…
Transferring BCI models from calibration to control: Observing shifts in EEG features
Ivo Pascal de Jong, Lüke Luna van den Wittenboer, Matias Valdenegro-Toro +1
Public Motor Imagery-based brain-computer interface (BCI) datasets are being used to develop increasingly good classifiers. However, they usually follow discrete paradigms where pa…
Uncertainty Quantification for cross-subject Motor Imagery classification
Prithviraj Manivannan, Ivo Pascal de Jong, Matias Valdenegro-Toro +1
Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining…
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro
Purpose: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) bi…