The Markov chain approximation method is an effective and widely used approach for computing optimal values and controls for stochastic systems. It was extended to nonlinear (and p...
We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between...
Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbe...
We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum en...
To facilitate accurate and efficient detection of motion
patterns in video data, it is desirable to abstract from pixel
intensity values to representations that explicitly and co...
: In this paper, we study the parameter estimation problem in a general heteroscedastic linear system, by putting the problem in the framework of the bilinear approach to low-rank ...