基于完全提升随机对偶理论的二元感知器容量

Mihailo Stojnic
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引用次数: 0

摘要

我们研究了具有一般阈值的经典二元感知器的统计能力$\kappa$。在认识到容量与双线性索引(bli)随机过程之间的联系之后,我们利用研究这类过程的最新进展来表征容量。特别是,我们依赖于\cite{Stojnicflrdt23}中建立的\emph{完全提升}的随机对偶理论(fl RDT)来创建研究感知机能力的一般框架。成功的基础数值评估是框架(最终整个RDT机器)成为完全实际操作所必需的。我们提出了在该方向上获得的结果,并发现在\emph{平稳化}全提升的第二级(第一非平凡)水平上实现了能力表征。所得结果与\cite{KraMez89}中通过统计物理复制方法得到的副本对称性破缺预测\emph{完全}吻合。最值得注意的是,对于著名的零阈值场景$\kappa=0$,我们发现了众所周知的$\alpha\approx0.8330786$缩放容量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Binary perceptrons capacity via fully lifted random duality theory
We study the statistical capacity of the classical binary perceptrons with general thresholds $\kappa$. After recognizing the connection between the capacity and the bilinearly indexed (bli) random processes, we utilize a recent progress in studying such processes to characterize the capacity. In particular, we rely on \emph{fully lifted} random duality theory (fl RDT) established in \cite{Stojnicflrdt23} to create a general framework for studying the perceptrons' capacities. Successful underlying numerical evaluations are required for the framework (and ultimately the entire fl RDT machinery) to become fully practically operational. We present results obtained in that directions and uncover that the capacity characterizations are achieved on the second (first non-trivial) level of \emph{stationarized} full lifting. The obtained results \emph{exactly} match the replica symmetry breaking predictions obtained through statistical physics replica methods in \cite{KraMez89}. Most notably, for the famous zero-threshold scenario, $\kappa=0$, we uncover the well known $\alpha\approx0.8330786$ scaled capacity.
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