Add to ORKGNetwork modeling is a critical component for building self-driving Software-Defined Networks.Traditional modeling solutions,such as simulation,are insufficient as they need a long time to run the simulations.We study how GNN models can help to solve network optimization problems such as routing
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most...
Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the pas...
Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a ne...
The advancing applications based on machine learning and deep learning in communication networks hav...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Re...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Network modeling is a critical component for building self-driving Software-Defined Networks, partic...
Today, network operators still lack functional network models able to make accurate predictions of e...
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most...
Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the pas...
Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a ne...
The advancing applications based on machine learning and deep learning in communication networks hav...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Re...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Network modeling is a critical component for building self-driving Software-Defined Networks, partic...
Today, network operators still lack functional network models able to make accurate predictions of e...
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most...
Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...