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.
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations.Nature machine intelligence, 6(10):1256–1269, 2024
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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.