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https://pubmed.ncbi.nlm.nih.gov/38113152
This study presents a method for generating high-quality prediction intervals (PIs) in neural networks by training two companion networks and introducing a novel loss function that balances minimizing PI width and ensuring PI integrity, resulting in higher quality PIs without compromising target estimation accuracy.