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arXiv 2504.08075cs.LOcs.LGmath.LO
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程序作为奇点

Programs as Singularities

Daniel Murfet, Will Troiani

AI总结:

本文通过连接线性逻辑与奇异学习理论,建立图灵机与实解析函数奇点的对应,证明贝叶斯后验可区分产生相同预测的不同算法实现,深化对奥卡姆剃刀的理解。

摘要:

我们基于线性逻辑中的Ehrhard-Regnier导数与Watanabe奇异学习理论中几何作用的联系,建立了图灵机结构与实解析函数奇点结构之间的对应关系。该对应通过将普通(离散)图灵机代码嵌入到形成光滑参数空间的噪声代码族中来实现。在此参数空间上,我们考虑一个以图灵机为临界点的势函数。通过将该势函数在临界点处的泰勒级数展开与错误综合征的组合学相关联,我们将局部几何与图灵机的内部结构联系起来。所考虑的势函数是统计模型的负对数似然,因此图灵机的结构及其相关奇点进一步与贝叶斯推断相关联。产生相同预测函数的两种算法可能对应具有不同几何形状的奇点,这意味着贝叶斯后验能够区分不同的算法实现,这与纯功能的推断观点相反。在奇异学习理论的背景下,我们的结果指向对奥卡姆剃刀以及归纳推断中简单性含义的更细致理解。

英文摘要:

We develop a correspondence between the structure of Turing machines and the structure of singularities of real analytic functions, based on connecting the Ehrhard-Regnier derivative from linear logic with the role of geometry in Watanabe's singular learning theory. The correspondence works by embedding ordinary (discrete) Turing machine codes into a family of noisy codes which form a smooth parameter space. On this parameter space we consider a potential function which has Turing machines as critical points. By relating the Taylor series expansion of this potential at such a critical point to combinatorics of error syndromes, we relate the local geometry to internal structure of the Turing machine. The potential in question is the negative log-likelihood for a statistical model, so that the structure of the Turing machine and its associated singularity is further related to Bayesian inference. Two algorithms that produce the same predictive function can nonetheless correspond to singularities with different geometries, which implies that the Bayesian posterior can discriminate between distinct algorithmic implementations, contrary to a purely functional view of inference. In the context of singular learning theory our results point to a more nuanced understanding of Occam's razor and the meaning of simplicity in inductive inference.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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