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At each iteration t≥1 , after each layer is applied: if (X(t))halt >0 , halt, otherwise, if (X(t))emit >0, emit1[(X (t))bit >0]

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cs.LG 1

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2026 1

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Neural Weight Norm = Kolmogorov Complexity

cs.LG · 2026-05-11 · unverdicted · novelty 7.0

Minimal weight norm of fixed-precision looped neural networks equals Kolmogorov complexity of output string up to log factor, making weight decay match the optimal universal prior up to polynomial factor.

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  • Neural Weight Norm = Kolmogorov Complexity cs.LG · 2026-05-11 · unverdicted · none · ref 42

    Minimal weight norm of fixed-precision looped neural networks equals Kolmogorov complexity of output string up to log factor, making weight decay match the optimal universal prior up to polynomial factor.