2 papers
cs.LG2026
RePAIR: Predictive Self-Supervised Representation Learning in Chess
Christoph Koller, Johannes Fürnkranz, Timo Bertram
In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synth…
cs.LG2024
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Yuanyuan Wang, Qian Song, Dawood Wasif +4
Uncertainty quantification (UQ) is essential for assessing the reliability of Earth observation (EO) products. However, the extensive use of machine learning models in EO introduce…