10 citations · 19 across the 6 of their papers we have counts for
6 papers
Differentiable Power-Flow Optimization
Muhammed Öz, Jasmin Hörter, Kaleb Phipps +3
With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conven…
Bayesian Lottery Ticket Hypothesis
Nicholas Kuhn, Arvid Weyrauch, Lars Heyen +3
Bayesian neural networks (BNNs) are a useful tool for uncertainty quantification, but require substantially more computational resources than conventional neural networks. For non-…
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert +4
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecastin…
Feed-Forward Optimization With Delayed Feedback for Neural Network Training
Katharina Flügel, Daniel Coquelin, Marie Weiel +3
Backpropagation has long been criticized for being biologically implausible due to its reliance on concepts that are not viable in natural learning processes. Two core issues are t…
Learning Tree Structures from Leaves For Particle Decay Reconstruction
James Kahn, Ilias Tsaklidis, Oskar Taubert +8
In this work, we present a neural approach to reconstructing rooted tree graphs describing hierarchical interactions, using a novel representation we term the Lowest Common Ancesto…
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Daniel Coquelin, Behnood Rasti, Markus Götz +3
As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hype…