45 citations · 47 across the 10 of their papers we have counts for
10 papers
Spectral deferred corrections parallelized across the method for differential-algebraic equations
Matthias Bolten, Lisa Wimmer
In this work, the performance of different spectral deferred corrections (SDC) methods applied to initial value problems for differential-algebraic equations (DAEs) of index one is…
DustNET: enabling machine learning and AI models of dusty plasmas
Zhehui Wang, Justin C. Burton, Niklas Dormagen +30
Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. De…
On the analysis of spectral deferred corrections for differential-algebraic equations of index one
Matthias Bolten, Lisa Wimmer
In this paper, we present a new spectral deferred corrections (SDC) method to solve semi-explicit differential-algebraic equations (DAEs) with the ability to be parallelized. The n…
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomp…
Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr +3
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows unce…
Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer +2
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…
Probabilistic Self-supervised Learning via Scoring Rules Minimization
Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer +4
In this paper, we propose a novel probabilistic self-supervised learning via Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic models to enhance repre…
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz +3
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning. However, deep en…
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Automated wildlife image classification: An active learning tool for ecological applications
Ludwig Bothmann, Lisa Wimmer, Omid Charrakh +6
Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first…