Sublinear neural networks parametrize convex sets by learning their support and gauge functions, backed by a universal approximation theorem and tested on shape optimization tasks.
Zico Kolter
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DMF augments kernel-based drifting models with scheduled friction to guarantee convergence and matches Optimal Flow Matching on FFHQ adult-to-child translation at 16x lower training cost.
citing papers explorer
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Parametrizing Convex Sets Using Sublinear Neural Networks
Sublinear neural networks parametrize convex sets by learning their support and gauge functions, backed by a universal approximation theorem and tested on shape optimization tasks.
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Attraction, Repulsion, and Friction: Introducing DMF, a Friction-Augmented Drifting Model
DMF augments kernel-based drifting models with scheduled friction to guarantee convergence and matches Optimal Flow Matching on FFHQ adult-to-child translation at 16x lower training cost.