Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
The UCLA Parallel Particle-in-Cell (UPIC) Framework, is designed to provide trusted components for building a variety of parallel Particle-in-Cell (PIC) codes. It is based on the ...
In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-...
It is difficult to render caustic patterns at interactive frame rates. This paper introduces new rendering techniques that relax current constraints, allowing scenes with moving, n...
Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend resea...
Sean M. McNee, Istvan Albert, Dan Cosley, Prateep ...