3 citations · 8 across the 14 of their papers we have counts for
8 papers · 1 filter
Memorisation bias in medical AI
Moritz A. Knolle, Martin J. Menten, Laurin Lux +5
Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their training datasets. While suc…
It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
Leonhard F. Feiner, Manuel Nickel, Martin Menten +6
Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. Thi…
Step-resolved data attribution for looped transformers
Georgios Kaissis, David Mildenberger, Juan Felipe Gomez +2
We study how individual training examples shape the internal computation of looped transformers, where a shared block is applied for recurrent iterations to enable latent reaso…
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Taha Emre, Arunava Chakravarty, Thomas Pinetz +9
Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recent state-of-the-art self-supervise…
Efficient numeracy in language models through single-token number embeddings
Linus Kreitner, Paul Hager, Jonathan Mengedoht +3
To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long calculations efficiently. This is…
On Arbitrary Predictions from Equally Valid Models
Sarah Lockfisch, Kristian Schwethelm, Martin Menten +4
Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In m…