The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...

A model of a long optical communication line consisting of alternating segments with anomalous and normal dispersion, whose lengths are picked up randomly from a certain interval, is considered. As the first stage of the analysis, we calculate small changes of parameters of a quasi-Gaussian pulse passing a double-segment cell by means of the variational approximation (VA) and approximate the ev...

Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests that inference networks can produce suboptimal variational parameters. We propose a hybrid approach, to use AVI to initialize the variational par...

In this paper we study the soliton dynamics of a high-density Bose-Einstein condensate (BEC) subject to time-oscillating trap. The behavior BEC is described with modified Gross-Pitaevskii equation (mGPE) which takes into account three-body losses, atomic feeding and quantum fluctuations (up novel term). A variational approximation (VA) used Gaussian pulse in static double-well potential. Direct...

We study inference and learning based on a sparse coding model with ‘spike-and-slab’ prior. As standard sparse coding, the used model assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the application of a more flexible ‘spike-and-slab’ distribution which models the absence ...

Gaussian process latent variable models (GPLVMs) are a probabilistic approach to modelling data that employs Gaussian process mapping from latent variables to observations. This paper revisits a recently proposed variational inference technique for GPLVMs and methodologically analyses the optimality and different parameterisations of the variational approximation. We investigate a structured va...