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of 49
pro vyhledávání: '"Lampe, Alexander"'
When deploying machine learning (ML) applications, the automated allocation of computing resources-commonly referred to as autoscaling-is crucial for maintaining a consistent inference time under fluctuating workloads. The objective is to maximize th
Externí odkaz:
http://arxiv.org/abs/2311.18659
Autor:
Mayer, Jana, Westermann, Johannes, Muriedas, Juan Pedro Gutiérrez H., Mettin, Uwe, Lampe, Alexander
In recent years, reinforcement learning (RL) has gained increasing attention in control engineering. Especially, policy gradient methods are widely used. In this work, we improve the tracking performance of proximal policy optimization (PPO) for arbi
Externí odkaz:
http://arxiv.org/abs/2107.09647
This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large nu
Externí odkaz:
http://arxiv.org/abs/1907.07890
Autor:
Lampe, Alexander.
Erlangen, Nürnberg, Univ., Diss., 2003.
Erscheinungsjahr an der Haupttitelstelle: 2002. Computerdatei im Fernzugriff.
Erscheinungsjahr an der Haupttitelstelle: 2002. Computerdatei im Fernzugriff.
Autor:
Müller, Ralf, Lampe, Alexander
Publikováno v:
In AEUE - International Journal of Electronics and Communications 2011 65(8):701-706
Autor:
Lampe, Alexander.
Nürnberg, University, Diss., 2003--Erlangen.
Erscheinungsjahr an der Haupttitelstelle: 2002.
Erscheinungsjahr an der Haupttitelstelle: 2002.
Akademický článek
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Autor:
Gay, Matthias, Kaden, Marika, Biehl, Michael, Lampe, Alexander, Villmann, Thomas, Merényi, Erzsébet, Mendenhall, Michael J., O'Driscoll, Patrick
Publikováno v:
Advances in Self-Organizing Maps and Learning Vector Quantization ISBN: 9783319285177
WSOM
Advances in Self-Organizing Maps and Learning Vector Quantization, 428, 293-303
WSOM
Advances in Self-Organizing Maps and Learning Vector Quantization, 428, 293-303
This paper addresses the application of gradient descent based machine learning methods to complex-valued data. In particular, the focus is on classification using Learning Vector Quantization and extensions thereof. In order to apply gradient-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9b633451972f6bf04df6cb957c443bc
https://doi.org/10.1007/978-3-319-28518-4_26
https://doi.org/10.1007/978-3-319-28518-4_26