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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MCBP detects boundaries by computing discrete mean curvature from k-nearest neighbor patches on the data manifold, then decomposes data into low-curvature smooth and high-curvature boundary subsets to improve clustering.
citing papers explorer
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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.
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A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning
MCBP detects boundaries by computing discrete mean curvature from k-nearest neighbor patches on the data manifold, then decomposes data into low-curvature smooth and high-curvature boundary subsets to improve clustering.