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.
Three approaches to the quantitative definition ofinformation’.Problems of information transmission, 1(1):1–7
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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.