On deep Gaussian mixture models in machine learning problems

The work is concentrated on the study of deep neural network architectures with implementation of mixtures of normal distributions in hidden layers for solving clustering and regression problems. The model with different sets of hyperparameters was compared to classical methods: k-means, linear regression, Gaussian mixture models (GMM), etc.