5 citations · 5 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
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…
Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty
Katharina Hechinger, Christoph Koller, Xiao Xiang Zhu +1
Uncertainty in machine learning models is a timely and vast field of research. In supervised learning, uncertainty can already occur in the first stage of the training process, the…
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?
Christoph Koller, Göran Kauermann, Xiao Xiang Zhu
Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the…