Research paper proposing a neural-based method for solving global optimization problems with noisy training samples through iterative refinement.
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Neural Global Optimization via Iterative Refinement from Noisy Samples
Neural networks can solve global optimization problems more robustly by iteratively refining solutions from noisy training samples, improving convergence in settings where exact data is unavailable.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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