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Covariance-based GNNs are fundamentally learnable generalizations of PCA, offering rigorous stability bounds against finite-sample noise that standard graph theory fails to capture.
Ditch slow diffusion policies: FMER achieves 7x faster training and superior performance in sparse reward RL by using flow matching and a tractable entropy regularization term.
