Search Results for author: Albert Cabellos-Aparicio

Found 17 papers, 8 papers with code

Bayesian inference of spatial and temporal relations in AI patents for EU countries

no code implementations18 Jan 2022 Krzysztof Rusek, Agnieszka Kleszcz, Albert Cabellos-Aparicio

In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour.

Bayesian Inference

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

Scaling Graph-based Deep Learning models to larger networks

no code implementations4 Oct 2021 Miquel Ferriol-Galmés, José Suárez-Varela, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.

Is Machine Learning Ready for Traffic Engineering Optimization?

1 code implementation3 Sep 2021 Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.

Multi-agent Reinforcement Learning

Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

1 code implementation30 Jul 2021 David Pujol-Perich, José Suárez-Varela, Albert Cabellos-Aparicio, Pere Barlet-Ros

To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.

Network Intrusion Detection

Applying Graph-based Deep Learning To Realistic Network Scenarios

no code implementations13 Oct 2020 Miquel Ferriol-Galmés, José Suárez-Varela, Pere Barlet-Ros, Albert Cabellos-Aparicio

Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems.

Radiation pattern prediction for Metasurfaces: A Neural Network based approach

no code implementations15 Jul 2020 Hamidreza Taghvaee, Akshay Jain, Xavier Timoneda, Christos Liaskos, Sergi Abadal, Eduard Alarcón, Albert Cabellos-Aparicio

Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98. 8%-99. 8%) and the time and computational complexity of an analytical model.

Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case

1 code implementation16 Oct 2019 Paul Almasan, José Suárez-Varela, Arnau Badia-Sampera, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate over network topologies seen during training, but not over new topologies.

Decision Making reinforcement-learning

RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

no code implementations3 Oct 2019 Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks.

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

3 code implementations23 Jan 2019 Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert Cabellos-Aparicio

In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0. 86$) when testing against topologies, routing and traffic not seen during training.

Networking and Internet Architecture

Understanding the Modeling of Computer Network Delays using Neural Networks

1 code implementation23 Jul 2018 Albert Mestres, Eduard Alarcón, Yusheng Ji, Albert Cabellos-Aparicio

In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance.

Cannot find the paper you are looking for? You can Submit a new open access paper.