摘要:受限制的玻尔兹曼机（RBM）是一种无向图模型.基于RBM的深度学习模型包括深度置信网（DBN）和深度玻尔兹曼机（DBM）等.在神经网络和RBM的训练过程中，过拟合问题是一个比较常见的问题.针对神经网络的训练，权值随机变量（weight random variables）、Dropout方法和早期停止方法已被用于缓解过拟合问题.首先，改变RBM模型中的训练参数，使用随机变量代替传统的实值变量，构建了基于随机权值的受限的波尔兹曼机（weight uncertainty RBM，简称WRBM），接下来，在WRBM基础上构建了相应的深度模型：Weight uncertainty Deep Belief Network（WDBN）和Weight uncertainty Deep Boltzmann Machine（WDBM），并且通过实验验证了WDBN和WDBM的有效性.最后，为了更好地建模输入图像，引入基于条件高斯分布的RBM模型，构建了基于spike-and-slab RBM（ssRBM）的深度模型，并通过实验验证了模型的有效性.



Abstract:Based on the restricted Boltzmann machine (RBM), which is a probabilistic graphical model, deep learning models contain deep belief net (DBN) and deep Boltzmann machine (DBM). The overfitting problems commonly exist in neural networks and RBM models. In order to alleviate the overfitting problem, this paper introduces weight random variables to the conventional RBM model and, then builds weight uncertainty deep models based on maximum likelihood estimation. In the experimental section, the paper verifies the effectiveness of the weight uncertainty RBM. In order to improve the image recognition ability, the paper introduces the spike-and-slab RBM (ssRBM) to weight uncertainty RBM and then builds the deep models. The experiments show that the deep models based on weight random variables are effective.



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