We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are d...
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
See a PPT file with videos at www.research.microsoft.com/users/jojic/FlexiblesSprites.htm We propose a technique for automatically learning layers of "flexible sprites" ...
There has been a great deal of interest in the past few years on ranking of results of queries on structured databases, including work on probabilistic information retrieval, rank...
Gautam Das, Vagelis Hristidis, Nishant Kapoor, S. ...
We present Hintikka games for formulae of the probabilistic temporal logic PCTL and countable labeled Markov chains as models, giving an operational account of the denotational se...
Harald Fecher, Michael Huth, Nir Piterman, Daniel ...