1 code implementation • 14 Apr 2024 • Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein, Thomas Gabor
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution.
no code implementations • 7 Jan 2024 • Robert Müller, Hasan Turalic, Thomy Phan, Michael Kölle, Jonas Nüßlein, Claudia Linnhoff-Popien
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
no code implementations • 9 Nov 2023 • Michael Kölle, Felix Topp, Thomy Phan, Philipp Altmann, Jonas Nüßlein, Claudia Linnhoff-Popien
We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
no code implementations • 28 Jun 2023 • Michael Kölle, Steffen Illium, Maximilian Zorn, Jonas Nüßlein, Patrick Suchostawski, Claudia Linnhoff-Popien
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular.
1 code implementation • 26 Apr 2023 • Philipp Altmann, Fabian Ritz, Leonard Feuchtinger, Jonas Nüßlein, Claudia Linnhoff-Popien, Thomy Phan
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
no code implementations • 30 Dec 2022 • Jonas Stein, Dominik Ott, Jonas Nüßlein, David Bucher, Mirco Schoenfeld, Sebastian Feld
The analysis of network structure is essential to many scientific areas, ranging from biology to sociology.
no code implementations • 20 Dec 2022 • Steffen Illium, Gretchen Griffin, Michael Kölle, Maximilian Zorn, Jonas Nüßlein, Claudia Linnhoff-Popien
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
1 code implementation • 24 Jun 2022 • Jonas Nüßlein, Christoph Roch, Thomas Gabor, Jonas Stein, Claudia Linnhoff-Popien, Sebastian Feld
A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods.
1 code implementation • 12 Jun 2022 • Jonas Nüßlein, Steffen Illium, Robert Müller, Thomas Gabor, Claudia Linnhoff-Popien
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.