DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.
Physics-informed neural particle flow for the bayesian update step
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PA-PINPF adds Deep Sets population encoders (state or feature) to PINPF for better Bayesian posterior particle transport on range-measurement and TDOA tasks.
citing papers explorer
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DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables
DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.
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Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update
PA-PINPF adds Deep Sets population encoders (state or feature) to PINPF for better Bayesian posterior particle transport on range-measurement and TDOA tasks.