Zobrazeno 1 - 10
of 2 639
pro vyhledávání: '"P. Köppel"'
The paper discusses the legal requirements and implications of the processing of information and personal data for advertising purposes, particularly in the light of the "Planet49" decision of the European Court of Justice (ECJ) and the "Cookie Conse
Externí odkaz:
http://arxiv.org/abs/2407.03846
Fair Representation Learning (FRL) is a broad set of techniques, mostly based on neural networks, that seeks to learn new representations of data in which sensitive or undesired information has been removed. Methodologically, FRL was pioneered by Ric
Externí odkaz:
http://arxiv.org/abs/2407.03834
The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualization can be implemented. To tackle this question we conduct a randomize
Externí odkaz:
http://arxiv.org/abs/2407.03118
Current digital computers are about to hit basic physical boundaries with respect to integration density, clock frequencies, and particularly energy consumption. This requires the application of new computing paradigms, such as quantum and analog com
Externí odkaz:
http://arxiv.org/abs/2309.05598
Autor:
Tagiew, Rustam, Köppel, Martin, Schwalbe, Karsten, Denzler, Patrick, Neumaier, Philipp, Klockau, Tobias, Boekhoff, Martin, Klasek, Pavel, Tilly, Roman
Publikováno v:
8th International Conference on Robotics and Automation Engineering (ICRAE), Singapore, Singapore, 2023, pp. 270-276
To achieve a driverless train operation on mainline railways, actual and potential obstacles for the train's driveway must be detected automatically by appropriate sensor systems. Machine learning algorithms have proven to be powerful tools for this
Externí odkaz:
http://arxiv.org/abs/2305.03001
Autor:
Köppel, Marius
The Mu3e experiment at the Paul Scherrer Institute (PSI) searches for the charged lepton flavour violating decay $\mu^+ \rightarrow e^+ e^+ e^-$. The experiment aims for an ultimate sensitivity of one in $10^{16}$ $\mu$ decays. The first phase of the
Externí odkaz:
http://arxiv.org/abs/2208.13508
Representation learning algorithms offer the opportunity to learn invariant representations of the input data with regard to nuisance factors. Many authors have leveraged such strategies to learn fair representations, i.e., vectors where information
Externí odkaz:
http://arxiv.org/abs/2208.02656
Autor:
Köppel, Marius
The Mu3e experiment at the Paul Scherrer Institute searches for the charged lepton flavor violating decay $\mu^+ \rightarrow e^+ e^+ e^-$. The experiment aims for an ultimate sensitivity of one in $10^{16}$ $\mu$ decays. The first phase of the experi
Externí odkaz:
http://arxiv.org/abs/2203.07855
Autor:
Cerrato, Mattia, Coronel, Alesia Vallenas, Köppel, Marius, Segner, Alexander, Esposito, Roberto, Kramer, Stefan
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various representation deb
Externí odkaz:
http://arxiv.org/abs/2202.03078
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various "representation de
Externí odkaz:
http://arxiv.org/abs/2201.06343