2 citations · 2 across the 8 of their papers we have counts for
9 papers
MIMIC-Note-Extract: A Large-Scale Structured Dataset Extracted from MIMIC-IV-Note Discharge Summaries
Kristian Schwethelm, Jonas Kuntzer, Conrad Ketzer +6
MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records, yet clinically important information, such as past medical history,…
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 transformer in equivalent unique parameters. From an iso-depth pretraining sweep across recurrence counts $r \in \{1, 2, 4,…
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…
Laplace Sample Information: Data Informativeness Through a Bayesian Lens
Johannes Kaiser, Kristian Schwethelm, Daniel Rueckert +1
Accurately estimating the informativeness of individual samples in a dataset is an important objective in deep learning, as it can guide sample selection, which can improve model e…
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer +3
Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. Howe…
Visual Privacy Auditing with Diffusion Models
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle +3
Data reconstruction attacks on machine learning models pose a substantial threat to privacy, potentially leaking sensitive information. Although defending against such attacks usin…
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
Anneliese Riess, Kristian Schwethelm, Johannes Kaiser +4
The gold standard for privacy in machine learning, Differential Privacy (DP), is often interpreted through its guarantees against membership inference. However, translating DP budg…
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
Ahmad Bdeir, Kristian Schwethelm, Niels Landwehr
Real-world visual data exhibit intrinsic hierarchical structures that can be represented effectively in hyperbolic spaces. Hyperbolic neural networks (HNNs) are a promising approac…