One aim of Meta-learning techniques is to minimize the time needed for problem solving, and the effort of parameter hand-tuning, by automating algorithm selection. The predictive m...
We consider the problem of convolutive blind source separation of stereo mixtures. This is often tackled using frequency-domain independent component analysis (FDICA), or time-fre...
Maria G. Jafari, Emmanuel Vincent, Samer A. Abdall...
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
In this article we show how an active stereo camera head can be made to autonomously learn to fixate objects in space. During fixation, the system performs an initial and a corre...
Current technologies aimed at supporting processes – whether it is a business or learning process – primarily follow a metadata- and data-centric paradigm. Whereas process met...