Search Results for author: Fabien Geyer

Found 4 papers, 2 papers with code

Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML

no code implementations7 Feb 2022 Fabien Geyer, Alexander Scheffler, Steffen Bondorf

FP therefore does not scale and has been out of reach for the exhaustive analysis of large networks.

RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

no code implementations28 Jan 2022 Krzysztof Rusek, Piotr Boryło, Piotr Jaglarz, Fabien Geyer, Albert Cabellos, Piotr Chołda

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths.

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

1 code implementation29 Dec 2021 José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).

Recommendation Systems

On the Robustness of Deep Learning-predicted Contention Models for Network Calculus

1 code implementation24 Nov 2019 Fabien Geyer, Steffen Bondorf

The network calculus (NC) analysis takes a simple model consisting of a network of schedulers and data flows crossing them.

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