Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, y...
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Affective reasoning holds great potential for interactive digital entertainment, education, and training. Incorporating affective reasoning into the decision-making capabilities o...
Animated pedagogical agents offer promise as a means of making computer-aided learning more engaging and effective. To achieve this, an agent must be able to interact with the lea...
W. Lewis Johnson, Erin Shaw, Andrew Marshall, Cath...