We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequenti...
When high-dimensional data vectors are visualized on a two- or three-dimensional display, the goal is that two vectors close to each other in the multi-dimensional space should als...
Petri Kontkanen, Jussi Lahtinen, Petri Myllymä...
In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse mat...
Csaba Benedek, Tamas Sziranyi, Zoltan Kato, and Jo...
TheexactlikelihoodfunctionofaGaussianvectorautoregressive-movingaverage(VARMA)model is evaluated in two nonstandard cases: (a) a parsimonious structured form, such as obtained in ...