ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.
Nature Reviews Neuroscience , volume=
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GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
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ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.
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Group-Aware Matrix Estimation and Latent Subspace Recovery
GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.