Hello, my name is Mlompapesh and today I would like to present our paper entitled Tradusing the Cost of Itting Mixed-Thered Models via Stochastic Sampling. Find it mixer models represent a fundamental class of machine learning models. They are suitable to cluster a finite number of data into a finite number of categories. Their learning capacity depends on a concrete form of the component distributions. Specific examples include a mixture of Gauss and distributions, a mixture of mixture distributions, a mixture of flow models. In all cases, training these models can be computationally expensive. This happens, for example, when there is high dimension of data, large number of components, or after adopting flow models, containing neural networks with a large number of hidden layers. Our aim is to design an algorithm which can reduce the computational cost of training these models. The standard way of training mixture models is via the EM algorithm. At each iteration, this algorithm computes the posterior distribution over all data points and all possible values of the latent variable. However, consider this simple example of training a Gaussian mixed-thermodel with five components, where the gray and black colors show the posterior probability equal to 1 and 0 respectively. If the other colors displaying the values in between, we can see that a lot of computations are wasted scenes for each component. Most of the posterior probabilities are equal to 0, but the algorithm uses them anyway. To address this problem, we propose to approximate the expected value in the E step via sampling the latent variables from the Metropolis Hastings kernel. This allows us to draw as little as a single sample of the latent variable per data point and thus perform a computations just once per data point, contrary to evaluating all components as in the EM algorithm. We applied this approach to the mixtures of real-valued non-volume preserving flows, achieving up to 350 times quicker training compared to the EM algorithm, while preserving or slightly improving the test look like we would. Thank you for watching, and I'm looking forward to meet you at the post session.