Hopfield网络推理的状态采样依赖性
State sampling dependence of the Hopfield network inference
摘要：
完全连接的Hopfield网络推断所观察到的磁化和成对的相关性的基础上。我们目前的系统在玻璃相与低温和高记忆负荷。我们发现，推理误差是非常敏感的状态采样的形式。当对单个状态进行采样以计算磁化和相关性时，无论采样状态如何，推断误差几乎是不可区分的。然而，如果数据是通过状态转换来收集的，则可以大大减少错误。我们的结果适用于不同的无序样本，并解释了先前观察到的低温下推理误差的大波动。
英文摘要：
The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations. We present the system in the glassy phase with low temperature and high memory load. We find that the inference error is very sensitive to the form of state sampling. When a single state is sampled to compute magnetizations and correlations, the inference error is almost indistinguishable irrespective of the sampled state. However, the error can be greatly reduced if the data is collected with state transitions. Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.
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