12 citations · 16 across the 18 of their papers we have counts for
16 papers
Uncertainty Identifies Difficult Samples Across Methods: A Multi-Task Study on a Heterogeneous Skin Lesion Dataset
Leon Koole, Jiapan Guo, Matias Valdenegro-Toro
Skin lesion classifiers can be confidently wrong on the cases that matter most, so knowing when a prediction should not be trusted is clinically as useful as the prediction. We stu…
YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Miro Miranda, Deepak Pathak, Patrick Helber +10
Crop yield prediction requires substantial data to train scalable models. However, creating yield prediction datasets is constrained by high acquisition costs, heterogeneous data q…
Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
Miro Miranda, Marcela Charfuelan, Matias Valdenegro Toro +1
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivi…
Forecasting Smog Clouds With Deep Learning
Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro
In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & P…
The Dilemma of Uncertainty Estimation for General Purpose AI in the EU AI Act
Matias Valdenegro-Toro, Radina Stoykova
The AI act is the European Union-wide regulation of AI systems. It includes specific provisions for general-purpose AI models which however need to be further interpreted in terms…
Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models
Kumud Lakara, Matias Valdenegro-Toro
Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in th…