The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics. In spite of the strong merit of interpretability, unavailability of backpropagation makes it less competitive than other generative models. Here we derive differentiable loss functions for both binary and multinary RBMs. Then we demonstrate their learnability and performance by generating colored face images.

翻译：限制的Boltzmann机器(RBM)是一种基于统计力学概念的具有代表性的基因模型。 尽管解释的优点很强,但缺乏反向性能使它比其他基因模型竞争力低。 在这里,我们从二进制和多元制减压中得出了不同的损失功能。 然后,我们通过生成彩色面部图像来证明它们的可学习性和性。