29 citations · 57 across the 7 of their papers we have counts for
9 papers
Quantitative probing: Validating causal models using quantitative domain knowledge
Daniel Grünbaum, Maike L. Stern, Elmar W. Lang
We present quantitative probing as a model-agnostic framework for validating causal models in the presence of quantitative domain knowledge. The method is constructed as an analogu…
Parameterized Reinforcement Learning for Optical System Optimization
Heribert Wankerl, Maike L. Stern, Ali Mahdavi +2
Designing a multi-layer optical system with designated optical characteristics is an inverse design problem in which the resulting design is determined by several discrete and cont…
A Graph Neural Network Framework for Causal Inference in Brain Networks
Simon Wein, Wilhelm Malloni, Ana Maria Tomé +5
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial…
Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps
Martin Stetter, Elmar W. Lang
Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. Humans seem to learn rich representat…
Electricity Load Forecasting -- An Evaluation of Simple 1D-CNN Network Structures
Christian Lang, Florian Steinborn, Oliver Steffens +1
This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN a…
Decomposing Temperature Time Series with Non-Negative Matrix Factorization
Peter Weiderer, Ana Maria Tomé, Elmar Wolfgang Lang
During the fabrication of casting parts sensor data is typically automatically recorded and accumulated for process monitoring and defect diagnosis. As casting is a thermal process…