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+ Silent errors in deep learning training can silently waste + thousands of GPU hours and produce low-quality models. We + introduce TrainCheck, a proactive checking framework that learns + semantic invariants from correct training runs and enforces them + at runtime to catch failures early—before they silently + accumulate cost and damage model reliability. +
+ +