SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization
cs.LG, stat.ML
Submitted: 2026-09-27
Updated: 2026-09-27
Code: https://github.com/amazonscience/probharde2e
Terminology
Sources
- Physics-Informed Neural Networks with Hard Linear Equality Constraints
- SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints
- HardNet++: Nonlinear Constraint Enforcement in Neural Networks
- Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
- Constrained Neural Parameterization for Optimization in Function Spaces
- Hard Constraint Projection in a Physics Informed Neural Network
- Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
- An introduction to sampling via measure transport
- HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees
- FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
- Soft-Radial Projection for Constrained End-to-End Learning
- LMI-Net: Linear Matrix Inequality--Constrained Neural Networks via Differentiable Projection Layers
- RAYEN: Imposition of Hard Convex Constraints on Neural Networks
- End-to-End Probabilistic Framework for Learning with Hard Constraints
- DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables
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