3 citations · 5 across the 5 of their papers we have counts for
6 papers
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
Gideon Stein, Niklas Penzel, Tristan Piater +1
Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiable assumptions and a lack of…
Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
Phillip Rothenbeck, Sai Karthikeya Vemuri, Niklas Penzel +1
The COVID-19 pandemic has highlighted the need for quantitative modeling and analysis to understand real-world disease dynamics. In particular, post hoc analyses using compartmenta…
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
Gideon Stein, Maha Shadaydeh, Jan Blunk +2
Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-…
Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients
Niklas Penzel, Joachim Denzler
Deep learning models achieve high predictive performance but lack intrinsic interpretability, hindering our understanding of the learned prediction behavior. Existing local explain…
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out
Tristan Piater, Niklas Penzel, Gideon Stein +1
A substantial body of research has focused on developing systems that assist medical professionals during labor-intensive early screening processes, many based on convolutional dee…
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
Niklas Penzel, Gideon Stein, Joachim Denzler
Deep models trained on large amounts of data often incorporate implicit biases present during training time. If later such a bias is discovered during inference or deployment, it i…