Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Mo...
Iain Murray, Ryan Prescott Adams, David J. C. MacK...
We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets. Our model simultaneously learns a set of properties of a ...
We present a survey of the various families of approaches to secure aggregation in distributed networks such as sensor networks. In our survey, we focus on the important algorithm...
Haowen Chan, Hsu-Chun Hsiao, Adrian Perrig, Dawn S...
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...
Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-...