Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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MLP training dynamics follow saddle-organized plateaus and near-optima before necessarily settling into an overfitting attractor on finite noisy datasets.
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Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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Plateaus, Optima, and Overfitting in Multi-Layer Perceptrons: A Saddle-Saddle-Attractor Scenario
MLP training dynamics follow saddle-organized plateaus and near-optima before necessarily settling into an overfitting attractor on finite noisy datasets.