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Training CNNs with random mini-batches can lead to slower convergence and a weak learning signal. A*-inspired batch selection can improve this. It's unclear if this holds up outside the lab.
“arXiv:2607.15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches. This creates two limitations: slower convergence, and a diminishing learning signal…”
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ACTIVE
CATEGORY
Models
EVIDENCE
Not yet assessed
ENTITY
Convolutional Neural Networks (CNNs), A*
DECISION
Automated · no editorial override
LAST OBSERVED
Aug 5, 2026