[PDF] Top 20 Decoupled Variational Gaussian Inference

... dual variational inference [10] both speed-up convergence, but only for a limited class of ...deterministic inference approximations exist as well. The local variational method is convex for ... See full document

... Bayesian inference on large datasets, much of the computational cost is due to calculating functions of the data – for example, gradients or ...Bayesian inference on a subset of the data. For example, ... See full document

... multivariate Gaussian approximations with varying rank ...a Gaussian approximation to include additional capacity can improve predictive performance in a BNN while retaining computational ... See full document

... Section 5.1.1, and its derivative needs to be computed. Second, these algorithms use approximate methods to evaluate the marginal variances that are required to drive local variational bound opti- misation. ... See full document

... collapsed variational Bayesian inference (CVB) algorithm for ...standard variational Bayesian inference, but offers almost the same performance as the stochastic al- gorithms due to its weak ... See full document

... (e.g., Gaussian, gamma and Dirichlet distribu- tions), a general mixture distribution and a few complex nodes for dynamic variables ...and Gaussian Markov ... See full document

... Following the recent advances in making automatic differentiation widely available and easy to use in practical systems (see e.g. Baydin et al., 2015), developments on stochastic variational inference for ... See full document

... The variational approach for lower bounding the marginal likelihood (and similar quantities) has been explored by several researchers in the past decade, and has received a lot of attention recently in the machine ... See full document

... Deep Gaussian processes provide a flexible approach to probabilistic modeling of data using either supervised or unsupervised ...tractable inference approximations to the marginal likelihood of the model ... See full document

... of Gaussian We verify the conclusions in Propo- sition 1 by using the same setting in that ...poor inference and a significantly over-estimated q φ (z), while InfoVAE demonstrates a ... See full document

... exact inference with this model has proved to be difficult because high-dimensional integrations are ...a variational approximation to this model, and we describe the optimization of the variational ... See full document

... representations of words have emerged that exhibit semantic consistency over directional metrics like cosine similarity. Neither categorical nor Gaussian observational distributions used in existing topic models ... See full document

... black–box variational Gaussian inference algorithms are available which can be applied to arbitrary link–functions, the choice of link–functions is not only crucial for defining the prior over ... See full document

... sequential inference methods for LFMs have also been developed (Hartikainen and S¨ arkk¨ a, ...multi-output Gaussian process (COGP) for handling large scale cases (Nguyen and Bonilla, ... See full document

... consider Gaussian process classification with a pro- bit inverse link function and suggest a variational Gaussian model that builds on inducing ...of variational inference with ... See full document

... In the experiments, a latent space variational distribution is required as initialisation. We use PCA to initialise the q−dimensional means. The variances are initialised to values around 0.5, which are considered ... See full document

... on Gaussian processes. Finally, the above dynamical variational GP-LVM algorithm can be easily extended to deal with datasets consisting of multiple independent sequences (probably of different length) such ... See full document

... full-rank Gaussian generalizes the mean-field Gaussian ...mean-field Gaussian; however, it comes at a computational ...a Gaussian. Choosing a Gaussian distribution may call to mind the ... See full document

... 2016; He et al., 2018) try to address this issue by providing new model architectures or by changing the objective functions. Our research lies in this second direction. We review the theory of VAE, and we argue that one ... See full document

... When the likelihood p(y | f , θ) is non-Gaussian, the posterior distribution of the latent function, condi- tional on observed data p(f | D, θ), does not have a closed form solution. A popular approach for ... See full document