受限玻尔兹曼机及其变体研究综述

As a typical probabilistic graphical model for learning data distribution and extracting intrinsic features, the restricted Boltzmann machine (RBM) is an important fundamental model in the field of deep learning. In recent years, numerous emerging models, i. e., RBM variants, have been obtained by improving the model structure and energy function of RBM, which can further enhance the feature extraction performance of the model. The study of RBM and its variants can significantly contribute to the development of the deep learning field and realize the rapid extraction of massive information in the era of big data. Based on this, the relevant research on RBM and its variants are systematically reviewed in recent years, and the improvement of training algorithm, model structure, deep model fusion research and the latest application are creatively reviewed. In particular, the focus is on sorting out the develop history of training algorithms and variants for RBM. Finally, the existing difficulties and challenges in the field of RBM and its variants are discussed, and the main research work is summarized and prospected.