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An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency.
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location
The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively.
A Survey on Computationally Efficient Neural Architecture Search
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs).
Federated Learning on Non-IID Data: A Survey
Federated learning is an emerging distributed machine learning framework for privacy preservation.
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