We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was m...
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
Many human action recognition tasks involve data that can be factorized into multiple views such as body postures and hand shapes. These views often interact with each other over ...
We present a framework for automatically summarizing social group activity over time. The problem is important in understanding large scale online social networks, which have dive...