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Lisa Wimmer
Lisa Wimmer
PhD student, University of Munich (LMU)
Verifisert e-postadresse på stat.uni-muenchen.de - Startside
Tittel
Sitert av
Sitert av
År
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
L Wimmer, Y Sale, P Hofman, B Bischl, E Hüllermeier
UAI 2023, 2023
682023
Automated wildlife image classification: An active learning tool for ecological applications
L Bothmann, L Wimmer, O Charrakh, T Weber, H Edelhoff, W Peters, ...
Ecological Informatics 77 (102231), 2023
192023
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
JG Wiese, L Wimmer, T Papamarkou, B Bischl, S Günnemann, ...
ECML-PKDD 2023, 2023
102023
Second-Order Uncertainty Quantification: Variance-Based Measures
Y Sale, P Hofman, L Wimmer, E Hüllermeier, T Nagler
82023
Diversified ensemble of independent sub-networks for robust self-supervised representation learning
A Vahidi, L Wimmer, HA Gündüz, B Bischl, E Hüllermeier, M Rezaei
ECML-PKDD 2024 (final version forthcoming), 2024
42024
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
E Sommer, L Wimmer, T Papamarkou, L Bothmann, B Bischl, D Rügamer
ICML 2024, 2024
42024
Probabilistic Self-supervised Learning via Scoring Rules Minimization
A Vahidi, S Schoßer, L Wimmer, Y Li, B Bischl, E Hüllermeier, M Rezaei
ICLR 2024, 2024
22024
Label-wise Aleatoric and Epistemic Uncertainty Quantification
Y Sale, P Hofman, T Löhr, L Wimmer, T Nagler, E Hüllermeier
UAI 2024, 2024
12024
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
L Wimmer, B Bischl, L Bothmann
arXiv preprint arXiv:2502.09137, 2025
2025
Quantifying aleatoric and epistemic uncertainty in medical image classification with deep neural networks
L Wimmer
University of Munich (LMU), 2021
2021
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Artikler 1–10