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arXiv 2303.06375cond-mat.dis-nn
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关于神经网络中的de Almeida-Thouless线

About the de Almeida-Thouless line in neural networks

Linda Albanese, Andrea Alessandrelli, Adriano Barra, Alessia Annibale

AI总结:

提出一种无需完整复制分析的严格方法,通过自由能展开检测复制对称破缺起始点,适用于多种无序系统,并推广了AT方法以处理不连续相变。

摘要:

在这项工作中,我们提出了一种严格且直接的方法来检测信息处理系统中复制对称理论不稳定性的起始点,该方法不需要像de Almeida和Thouless最初为自旋玻璃提出的方法那样进行完整的复制分析。该方法基于在一步复制对称破缺(RSB)框架下围绕RS值对自由能进行展开。因此,它仅需要自由能的连续性和可微性,并且能够稳健地广泛应用于具有淬火无序的系统。我们将该方法应用于Hopfield模型和具有多节点Hebbian相互作用的神经网络作为案例研究。在附录中,我们在Sherrington-Kirkpatrick模型和Ising P-自旋模型上测试了该方法,恢复了这些模型文献中已知的AT线,作为一个特殊极限,该极限对应于假设从RS到RSB相的转变可以通过连续变化序参数来获得。我们的方法提供了AT方法的推广,该方法不依赖于这一极限,并且可以应用于具有不连续相变的系统,正如我们在球形P-自旋模型上明确展示的那样,恢复了已知的RS不稳定性线。

英文摘要:

In this work we present a rigorous and straightforward method to detect the onset of the instability of replica-symmetric theories in information processing systems, which does not require a full replica analysis as in the method originally proposed by de Almeida and Thouless for spin glasses. The method is based on an expansion of the free-energy obtained within one-step of replica symmetry breaking (RSB) around the RS value. As such, it requires solely continuity and differentiability of the free-energy and it is robust to be applied broadly to systems with quenched disorder. We apply the method to the Hopfield model and to neural networks with multi-node Hebbian interactions, as case studies. In the appendices we test the method on the Sherrington-Kirkpatrick and the Ising P-spin models, recovering the AT lines known in the literature for these models, as a special limit, which corresponds to assuming that the transition from the RS to the RSB phase can be obtained by varying continuously the order parameters. Our method provides a generalization of the AT approach, which does not rely on this limit and can be applied to systems with discontinuous phase transitions, as we show explicitly for the spherical P-spin model, recovering the known RS instability line.

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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