Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Mayer, Rubén"'
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices w
Externí odkaz:
http://arxiv.org/abs/2409.11129
The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in developing and deploying machine learning applications to avoid substantial fines, prioritizing private and secure data processing with data remaining at it
Externí odkaz:
http://arxiv.org/abs/2407.08105
Similar to other transaction processing frameworks, blockchain systems need to be dynamically reconfigured to adapt to varying workloads and changes in network conditions. However, achieving optimal reconfiguration is particularly challenging due to
Externí odkaz:
http://arxiv.org/abs/2406.06318
In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling collaborative
Externí odkaz:
http://arxiv.org/abs/2404.02779
Autor:
Woisetschläger, Herbert, Erben, Alexander, Marino, Bill, Wang, Shiqiang, Lane, Nicholas D., Mayer, Ruben, Jacobsen, Hans-Arno
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML
Externí odkaz:
http://arxiv.org/abs/2402.05968
Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, the ability to predict their pe
Externí odkaz:
http://arxiv.org/abs/2402.04874
Autor:
Woisetschläger, Herbert, Isenko, Alexander, Wang, Shiqiang, Mayer, Ruben, Jacobsen, Hans-Arno
Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models on
Externí odkaz:
http://arxiv.org/abs/2401.04472
With the increased adaption of blockchain technologies, permissioned blockchains such as Hyperledger Fabric provide a robust ecosystem for developing production-grade decentralized applications. However, the additional latency between executing and c
Externí odkaz:
http://arxiv.org/abs/2310.15988
Autor:
Ajwani, Deepak, Bisseling, Rob H., Casel, Katrin, Çatalyürek, Ümit V., Chevalier, Cédric, Chudigiewitsch, Florian, Faraj, Marcelo Fonseca, Fellows, Michael, Gottesbüren, Lars, Heuer, Tobias, Karypis, George, Kaya, Kamer, Lacki, Jakub, Langguth, Johannes, Li, Xiaoye Sherry, Mayer, Ruben, Meintrup, Johannes, Mizutani, Yosuke, Pellegrini, François, Petrini, Fabrizio, Rosamond, Frances, Safro, Ilya, Schlag, Sebastian, Schulz, Christian, Sharma, Roohani, Strash, Darren, Sullivan, Blair D., Uçar, Bora, Yzelman, Albert-Jan
Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application c
Externí odkaz:
http://arxiv.org/abs/2310.11812
Autor:
Woisetschläger, Herbert, Isenko, Alexander, Wang, Shiqiang, Mayer, Ruben, Jacobsen, Hans-Arno
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. However, training or fine-tuning
Externí odkaz:
http://arxiv.org/abs/2310.03150