LBW-Guard is a bounded autonomous control layer above AdamW that improves stability, reduces perplexity, and speeds up training for Qwen2.5 models under learning-rate stress on WikiText-103.
Understanding the difficulty of training deep feedforward neural networks
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UGDA-Net raises Dice coefficient by 9.3% over baseline on a 432-image plant seedling dataset by combining uncertainty-guided attention, entropy-weighted loss, and deep supervision.
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Learn-by-Wire Training Control Governance: Bounded Autonomous Training Under Stress for Stability and Efficiency
LBW-Guard is a bounded autonomous control layer above AdamW that improves stability, reduces perplexity, and speeds up training for Qwen2.5 models under learning-rate stress on WikiText-103.
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Uncertainty-Guided Attention and Entropy-Weighted Loss for Precise Plant Seedling Segmentation
UGDA-Net raises Dice coefficient by 9.3% over baseline on a 432-image plant seedling dataset by combining uncertainty-guided attention, entropy-weighted loss, and deep supervision.