The article proposes that riddled basin geometry is a fundamental mechanism limiting the predictability of deep learning training outcomes. It shows that initialization points leading to different solutions are arbitrarily close, forming a fat fractal structure. This geometry results in near-zero uncertainty exponents, meaning increased precision in initialization yields only marginal gains in reproducibility. The findings apply across various architectures and optimizers, suggesting a universal limit to reliable AI deployment.
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