… [37] proposed a fast two-stage algorithm for nonnegative matrix factorization in streaming
data. In this article, the standard variational Bayes formulation is adapted to online settings by …

… variational log-likelihood uses a variational distribution q(z). Variational Bayes and ideal
variational Bayes… While earlier applications of variational inference appealed to variational EM …

… variational Bayesian inference. In addition, the model simultaneously achieves low-rankness
through sparse Bayesian … variational Bayesian (VB) model for matrix low-rank and sparse …

… Bayesian deep learning-based model treatment, namely, variational autoencoder Bayesian
matrix factorization … The proposed model uses stochastic gradient variational Bayes to …

… general Empirical Bayes approach to matrix factorization (EBMF)… prior distributions from the
observed data. The approach is very … in detail the variational approach to the K factor model, …

… Because these distributions are intractable, we propose novel collapsed Gibbs sampling
and collapsed variational inference strategies. We also derive a nonparametric approximation …

… Our model consists of two end-to-end variational autoencoder neural networks, namely user
… our proposed variational inference. We present a Stochastic Gradient Variational Bayes …

… In the second section, we briefly explain Bayesian inference and the variational Bayesian
algorithm. In the third section, we present the Main Theorems and sketches of their proofs. In …

… Here, J captures the temporal structure of the underlying subspace, and is learned from the
data itself. The scaling ambiguity present in matrix factorization allows the transition matrix J …

… We proposed a manifold-based Variational Bayes algorithm that takes into account both
information geometry and geometric structure of the constraint parameter space. The algorithm …