6 citations · 12 across the 24 of their papers we have counts for
6 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…
Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching
Kristian Schwethelm, Daniel Rueckert, Georgios Kaissis
A main promise of looped language models is depth-adaptive inference. By looping a block of shared layers a variable number of times, the model can use less compute for "easy" toke…
How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models
Kristian Schwethelm, Daniel Rueckert, Georgios Kaissis
We measure how much one recurrence is worth to a looped (depth-recurrent) transformer, in equivalent unique parameters. From an iso-depth pretraining sweep across recurrence counts…
Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
Dmitrii Seletkov, Paul Hager, Georgios Kaissis +3
Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular cov…
Unintended Memorization of Sensitive Information in Fine-Tuned Language Models
Marton Szep, Jorge Marin Ruiz, Georgios Kaissis +4
Fine-tuning Large Language Models (LLMs) on sensitive datasets carries a substantial risk of unintended memorization and leakage of Personally Identifiable Information (PII), which…