2 citations · 5 across the 10 of their papers we have counts for
10 papers
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
Tim Rensmeyer, Oliver Niggemann
Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging wh…
A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle
Christoph Petroll, Sebastian Eilermann, Philipp Hoefer +1
One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and ge…
Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Lukas Moddemann, Henrik Sebastian Steude, Alexander Diedrich +1
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time se…
Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
Jan-Philipp Roche, Oliver Niggemann, Jens Friebe
Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables…
Graph Structural Residuals: A Learning Approach to Diagnosis
Jan Lukas Augustin, Oliver Niggemann
Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framewor…
A Diagnosis Algorithms for a Rotary Indexing Machine
Maria Krantz, Oliver Niggemann
Rotary Indexing Machines (RIMs) are widely used in manufacturing due to their ability to perform multiple production steps on a single product without manual repositioning, reducin…