多状态稀疏系统的推理与学习
Inference and learning in sparse systems with multiple states
摘要：
我们讨论了如何推断可以执行时，数据从非遍历阶段的系统与多个吸引子采样。我们以有限连通Hopfield模型为模型系统，在记忆阶段，提出了一种腔方法来重建数据分别从几个吸引子状态采样时的耦合。我们还展示了如何推理结果可以转换为神经网络的学习协议，其中模式是通过弱外部领域。该协议是简单和完全本地的，并且能够存储与输入模式有有限重叠的模式，而不会达到所有存储器丢失的自旋玻璃阶段。
英文摘要：
We discuss how inference can be performed when data are sampled from the non-ergodic phase of systems with multiple attractors. We take as model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin glass phase where all memories are lost.
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。
