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papersTODAY 04:00 UTC

Initial Weight Scale Found to Control Feature Specialization in ReLU Networks

A research paper examines how the scale of a network's initial weights influences which features individual ReLU neurons end up learning during training. The authors argue that this initialization-dependent effect, framed as a hidden gauge, helps explain why trained networks organize representations the way they do.

Feature specialization Gauge symmetry Neural network training ReLU networks Weight initialization representation learning

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIHidden Gauge Controls Feature Specialization in ReLU Networks ↗TODAY 04:00 UTC

arXiv cs.LGHidden Gauge Controls Feature Specialization in ReLU Networks ↗TODAY 04:00 UTC