This paper investigates theoretically based instructional approaches for organizational training, education and knowledge acquisition for simulation modeling. It proposes differen...
Tajudeen A. Atolagbe, Vlatka Hlupic, Simon J. E. T...
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
This contribution reports on ongoing collaborative research at the University of Stanford, Department of Psychology, and the University of Hamburg, Department for Informatics. Ext...
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...