A λ-convex variational surrogate for shallow NN training yields global well-posedness, almost C³ regularity, and an explicit linear-system solution with 1/α generalization and O(1/N) finite-width rates.
Sobolev acceleration for neural networks.arXiv preprint arXiv:2509.19773,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
citing papers explorer
-
Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks
A λ-convex variational surrogate for shallow NN training yields global well-posedness, almost C³ regularity, and an explicit linear-system solution with 1/α generalization and O(1/N) finite-width rates.