NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
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Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.
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NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
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Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.