When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter’s estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as th...

In this paper we describe a method for automatically animating interactive characters based on an existing corpus of key-framed hand-animation. The method learns separate low-dimensional embeddings for subsets of the hand-animation corresponding to different semantic labels. These embeddings use the Gaussian Process Latent Variable Model to map high-dimensional rig control parameters to a three...

Facial-expression data often appear in multiple views either due to head-movements or the camera position. Existing methods for multi-view facial expression recognition perform classification of the target expressions either by using classifiers learned separately for each view or by using a single classifier learned for all views. However, these approaches do not explore the fact that multi-vi...

The problem of learning the structure of a DAG model in the presence of latent variables presents many formidable challenges. In particular there are an innnite number of latent variable models to consider, and these models possess features which make them hard to work with. We describe a class of graphical models which can represent the conditional independence structure induced by a latent va...

Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-...

The purpose of the current review is to examine individual differences in i ntelligence and working memory capacity. The emphasis is on latent variable models and theoretical frameworks that connect interindividual differences in behavior with intraindividual psychological processes. Our review suggests that intelligence and working memory capacity are strongly correlated and that the shared va...

Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntacticsemantic dependency parsing, developed...