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Found 3 papers, 2 papers with code
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
In this paper, we present a novel formulation of Combinatorial Optimization Problems (COPs) as Markov Decision Processes (MDPs) that effectively leverages common symmetries of COPs to improve out-of-distribution robustness.
Combinatorial Optimization
Out-of-Distribution Generalization
On the Generalization of Neural Combinatorial Optimization Heuristics
Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems.
Structured Time Series Prediction without Structural Prior
Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.).
Ranked #9 on
Traffic Prediction
on PeMS07
(using extra training data)
