We study the prevalent problem when a test distribution differs from the training distribution. We consider a setting where our training set consists of a small number of sample d...
Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Fost...
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applicati...
This paper introduces a domain ontology to describe learning material that compose a course, capable of providing adaptive e-learning environments and reusable educational resourc...
The conversational agent understands and provides users with proper information based on natural language. Conventional agents based on pattern matching have much restriction to ma...
TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (...