… lower bound by training its parameters, and then we combine the lower bound of the variational
auto-… Variational bayesian inference with stochastic search. In Proceedings of the 29th …

… the variational Bayesian expectation maximization algorithm, which optimizes the evidence
lower bound on the complete-data … We demonstrate the performance on several artificial data …

… recovery method based on the variational Bayesian parallel factorization is proposed. Firstly,
the … by the parallel factor analysis theory. Meanwhile, combined with the Bayesian method, …

… The first is that SCE is proposed, and the variational inference oriented SCE is illustrated in
detail. … problems using nonnegative matrix factorization. In: Proc. of the 7th IEEE Int’l Conf. …

… This dissertation presents Bayesian models of microphone array processing for computational
auditory scene analysis in multisource environments. In such environments where …

… method based on the variational Bayesian theory that provides … research directions of the
variational Bayesian theory in the … , joint optimization, variational Bayesian theory Citation Pan …

低秩稀疏矩阵优化问题是一类带有组合性质的非凸非光滑优化问题. 由于零模与秩函数的重要性
和特殊性, 这类NP-难矩阵优化问题的模型与算法研究在过去〸几年里取得了长足发展. 本文从…

… (2016) used variational inference to construct a lower bound on the IB objective. It
demonstrated that models trained with such an objective outperform other methods in terms of …

… unlabeled data (untrained neural architectures) within the entire search space to train a
variational autoencoder with a self-supervised learning mechanism. The variational autoencoder …

… In summary, this chapter reviews the three main stages of data analysis: spatial or image
transforms, modelling and inference; these are the areas covered in the first three parts of this …