Zobrazeno 1 - 10
of 1 312
pro vyhledávání: '"Müller, Robert A."'
Autor:
Kölle, Michael, Ahouzi, Afrae, Debus, Pascal, Çetiner, Elif, Müller, Robert, Schuman, Daniëlle, Linnhoff-Popien, Claudia
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large datasets. In
Externí odkaz:
http://arxiv.org/abs/2407.20753
Cardinality-based feature models permit to select multiple copies of the same feature, thus generalizing the notion of product configurations from subsets of Boolean features to multisets of feature instances. This increased expressiveness shapes a-p
Externí odkaz:
http://arxiv.org/abs/2407.04499
Autor:
Müller, Robert, Turalic, Hasan, Phan, Thomy, Kölle, Michael, Nüßlein, Jonas, Linnhoff-Popien, Claudia
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this, we introduce ClusterComm, a fully decentralized MARL framework where
Externí odkaz:
http://arxiv.org/abs/2401.03504
Autor:
Kölle, Michael, Ahouzi, Afrae, Debus, Pascal, Müller, Robert, Schuman, Danielle, Linnhoff-Popien, Claudia
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class Support Vector Machine alongside a quantum kernel, known for its class
Externí odkaz:
http://arxiv.org/abs/2312.09174
Autor:
Stein, Jonas, Christ, Ivo, Kraus, Nicolas, Mansky, Maximilian Balthasar, Müller, Robert, Linnhoff-Popien, Claudia
Publikováno v:
QCE'23 Companion: Proceedings of the Companion IEEE International Conference on Quantum Computing and Engineering, 2023, 20-25
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly advancing field of Quantum Natural Language Processing (QNLP), we exp
Externí odkaz:
http://arxiv.org/abs/2307.11788
Autor:
Kölle, Michael, Giovagnoli, Alessandro, Stein, Jonas, Mansky, Maximilian Balthasar, Hager, Julian, Rohe, Tobias, Müller, Robert, Linnhoff-Popien, Claudia
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, su
Externí odkaz:
http://arxiv.org/abs/2306.05776
Autor:
Müller, Robert, Boutillon, Arthur, Jahn, Diego, Starruß, Jörn, David, Nicolas B., Brusch, Lutz
Collective cell migration is an important process during biological development and tissue repair but may turn malignant during tumor invasion. Mathematical and computational models are essential to unravel the mechanisms of self-organization that un
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A93526
https://tud.qucosa.de/api/qucosa%3A93526/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A93526/attachment/ATT-0/
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve compa
Externí odkaz:
http://arxiv.org/abs/2212.10093
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time. When there is no immediate relationship between subsequent data points (like when the data points are generated at
Externí odkaz:
http://arxiv.org/abs/2212.11085
Autor:
Schmid, Kyrill, Belzner, Lenz, Müller, Robert, Tochtermann, Johannes, Linnhoff-Popien, Claudia
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals. In these settings agents are likely to learn undesirable
Externí odkaz:
http://arxiv.org/abs/2207.07388