5 papers
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +2
Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a giv…
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simu…
Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real…
Ordered Topological Deep Learning: a Network Modeling Case Study
Guillermo Bernárdez, Miquel Ferriol-Galmés, Carlos Güemes-Palau +4
Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…