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2020 – today
- 2026
[j1]Niklas Penzel
, Daniel Scheliga, Hannes Oppermann, Patrick Mäder, Jens Haueisen, Joachim Denzler, Marco Seeland:
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets. J. Biomed. Informatics 177: 105010 (2026)- [c14]Niklas Penzel, Joachim Denzler:
Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients. WACV 2026: 7398-7408 - [i9]Gideon Stein, Niklas Penzel, Tristan Piater, Joachim Denzler:
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations. CoRR abs/2605.03045 (2026) - 2025
- [c13]Gideon Stein, Maha Shadaydeh, Jan Blunk, Niklas Penzel, Joachim Denzler:
CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series. ICLR 2025 - [c12]Laines Schmalwasser, Niklas Penzel, Joachim Denzler, Julia Niebling:
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks. ICML 2025 - [c11]Sven Sickert, Maria Gogolev, Niklas Penzel, Tim Büchner, Joachim Denzler:
Modifying Generative Distributions in Latent Diffusion Models to Improve Alignment with Desired Properties. MVA 2025: 1-6 - [i8]Niklas Penzel, Joachim Denzler:
Towards Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients. CoRR abs/2503.05424 (2025) - [i7]Gideon Stein, Maha Shadaydeh, Jan Blunk, Niklas Penzel, Joachim Denzler:
CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series. CoRR abs/2503.17452 (2025) - [i6]Laines Schmalwasser, Niklas Penzel, Joachim Denzler, Julia Niebling:
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks. CoRR abs/2505.17883 (2025) - [i5]Phillip Rothenbeck, Sai Karthikeya Vemuri, Niklas Penzel, Joachim Denzler:
Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks. CoRR abs/2510.06776 (2025) - 2024
- [c10]Tim Büchner, Niklas Penzel, Orlando Guntinas-Lichius, Joachim Denzler:
The Power of Properties: Uncovering the Influential Factors in Emotion Classification. ICPRAI (2) 2024: 440-448 - [c9]Niklas Penzel, Gideon Stein, Joachim Denzler:
Reducing Bias in Pre-Trained Models by Tuning While Penalizing Change. VISIGRAPP (2): VISAPP 2024: 90-101 - [c8]Tristan Piater, Niklas Penzel, Gideon Stein, Joachim Denzler:
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work out. VISIGRAPP (2): VISAPP 2024: 149-158 - [i4]Tim Büchner, Niklas Penzel, Orlando Guntinas-Lichius, Joachim Denzler:
The Power of Properties: Uncovering the Influential Factors in Emotion Classification. CoRR abs/2404.07867 (2024) - [i3]Niklas Penzel, Gideon Stein, Joachim Denzler:
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change. CoRR abs/2404.12292 (2024) - [i2]Tristan Piater, Niklas Penzel, Gideon Stein, Joachim Denzler:
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out. CoRR abs/2404.12295 (2024) - [i1]Tim Büchner, Niklas Penzel, Orlando Guntinas-Lichius, Joachim Denzler:
Facing Asymmetry - Uncovering the Causal Link between Facial Symmetry and Expression Classifiers using Synthetic Interventions. CoRR abs/2409.15927 (2024) - 2023
- [c7]Jan Blunk, Niklas Penzel, Paul Bodesheim, Joachim Denzler:
Beyond Debiasing: Actively Steering Feature Selection via Loss Regularization. DAGM 2023: 394-408 - [c6]Niklas Penzel, Jana Kierdorf, Ribana Roscher, Joachim Denzler:
Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances. ICCV (Workshops) 2023: 572-581 - [c5]Niklas Penzel, Joachim Denzler:
Interpreting Art by Leveraging Pre-Trained Models. MVA 2023: 1-6 - 2022
- [c4]Tim Büchner, Niklas Penzel, Orlando Guntinas-Lichius, Joachim Denzler:
Facing Asymmetry - Uncovering the Causal Link Between Facial Symmetry and Expression Classifiers Using Synthetic Interventions. ACCV (4) 2022: 443-464 - [c3]Niklas Penzel, Christian Reimers, Paul Bodesheim, Joachim Denzler:
Investigating Neural Network Training on a Feature Level Using Conditional Independence. ECCV Workshops (6) 2022: 383-399 - 2021
- [c2]Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification. CVPR Workshops 2021: 1810-1819 - [c1]Niklas Penzel, Christian Reimers, Clemens-Alexander Brust, Joachim Denzler:
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning. GCPR 2021: 159-173
Coauthor Index
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