We provide a novel view of learning an approximate model of a partially observable environment from data and present a simple implemenf the idea. The learned model abstracts away ...
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain la...
Matrix factorization into the product of lowrank matrices induces non-identifiability, i.e., the mapping between the target matrix and factorized matrices is not one-to-one. In th...
In a trend that reflects the increasing demand for intelligent applications driven by business data, IBM today is building out a significant number of applications that leverage m...
For centuries, scholars have explored the deep links among human languages. In this paper, we present a class of probabilistic models that use these links as a form of naturally o...