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Found 5 papers, 2 papers with code
Efficient and Mathematically Robust Operations for Certified Neural Networks Inference
In recent years, machine learning (ML) and neural networks (NNs) have gained widespread use and attention across various domains, particularly in transportation for achieving autonomy, including the emergence of flying taxis for urban air mobility (UAM).
Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
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
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
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
On the Robustness of Deep Learning-predicted Contention Models for Network Calculus
The network calculus (NC) analysis takes a simple model consisting of a network of schedulers and data flows crossing them.
