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.
Bridging kolmogorov com- plexity and deep learning: Asymptotically optimal description length objectives for transformers
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Neural Weight Norm = Kolmogorov Complexity
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.