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papersSEP 10 04:00 UTC

Edge-of-chaos initialization fails for higher input derivatives in wide networks

New research indicates that while the edge-of-chaos initialization scheme keeps first-order input perturbations stable in very wide randomly initialized networks, higher-order input derivatives become unstable under the same setup. Because techniques such as physics-informed losses, score matching, and derivative regularization rely on those higher derivatives, the results expose a gap in how such networks should be initialized for derivative-based training. The analysis focuses on smooth fully connected networks with scalar inputs.

theory/neural-networks/initialization/training