There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative ...
Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the dis...
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabiliti...
There is a large gap between the theory and practice for random number generation. For example, on most operating systems, using /dev/random to generate a 256-bit AES key is highl...
We introduce a new method of a biped walking pattern generation by using a preview control of the zeromoment point (ZMP). First, the dynamics of a biped robot is modeled as a runn...