We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack ...
In this paper, we propose a comprehensive probabilistic framework which can be used to model and analyze cognitive radio (CR) network using carrier sensing (CS) based multiple acc...
We significantly improve the computational efficiency of a probabilistic approach for multiple pitch tracking. This method is based on a factorial hidden Markov model and two al...