A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
A simple yet effective baseline for non-attributed graph classification
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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PyTorch Geometric is a PyTorch library that delivers fast graph neural network training through sparse GPU kernels and variable-size mini-batching.
A GNN is trained to predict adaptive step sizes and weights for distributed ADMM by unrolling a fixed number of iterations and minimizing solution error on a problem class.
citing papers explorer
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Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
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Fast Graph Representation Learning with PyTorch Geometric
PyTorch Geometric is a PyTorch library that delivers fast graph neural network training through sparse GPU kernels and variable-size mini-batching.
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Learning to accelerate distributed ADMM using graph neural networks
A GNN is trained to predict adaptive step sizes and weights for distributed ADMM by unrolling a fixed number of iterations and minimizing solution error on a problem class.