论文标题
深玻尔兹曼机器机电研究在通过单数值分解训练后给定重量参数
Statistical-mechanical study of deep Boltzmann machine given weight parameters after training by singular value decomposition
论文作者
论文摘要
近年来，依赖多层网络的深度学习方法已在广泛的领域中进行了积极研究，而深玻尔兹曼机器（DBMS）就是其中之一。在这项研究中，理论上通过基于复制方法来研究了具有通过学习获得的一些适当权重参数的DBM模型。表征DBM作为发电机及其相图的作用的阶段是有意义地取决于每一层中的图层和单元的数量的。特别是发现，当隐藏层中的权重参数之间的相关性起着至关重要的作用，并且当相关性小于某个阈值时，层编号的增加对DBM作为发电机的性能具有负面影响。
Deep learning methods relying on multi-layered networks have been actively studied in a wide range of fields in recent years, and deep Boltzmann machines(DBMs) is one of them. In this study, a model of DBMs with some properites of weight parameters obtained by learning is studied theoretically by a statistical-mechanical approach based on the replica method. The phases characterizing the role of DBMs as a generator and their phase diagram are derived, depending meaningfully on the numbers of layers and units in each layer. It is found, in particular, that the correlation between the weight parameters in the hidden layers plays an essential role and that an increase in the layer number has a negative effect on DBM's performance as a generator when the correlation is smaller than a certain threshold value.
