X Zhang, S Song, L Zhu, K You, C Wu - Science China Information …, 2016 - Springer
… the variationalBayesian 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 variationalBayesian 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 variationalinference oriented SCE is illustrated in detail. … problems using nonnegative matrixfactorization. In: Proc. of the 7th IEEE Int’l Conf. …
T Otsuka - Journal of the Acoustical Society of America, 1973 - core.ac.uk
… 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 variationalBayesian theory that provides … research directions of the variationalBayesian theory in the … , joint optimization, variationalBayesian theory Citation Pan …
… (2016) used variationalinference 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 …