AI RESEARCH
ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection
arXiv CS.LG
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ArXi:2502.06774v4 Announce Type: replace Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference accuracy. Despite the nonlinear nature of most real-world tasks, the majority of existing methods are limited to affine (equality) or convex (inequality) constraints. We