Linking Speaking and Looking Behavior Patterns with Group Composition, Perception, and Performance
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In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...
We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. ...
In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to probabilistic topic models. We use an unsupervised learning approach that dis ...
In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, ...
Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). Th ...
Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). Th ...
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of m ...
We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. ...
In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...
In this report, we propose a statistical model to deal with the discrete-distribution data varying over time. The proposed model -- HMM+DM -- extends the Dirichlet mixture model to the dynamic case: Hidden Markov Model with Dirichlet mixture output. Both t ...