A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Abstract This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The lea...
Adam L. Berger, Rich Caruana, David Cohn, Dayne Fr...
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
Students learn more through hands-on project experience for computer science courses such as operating systems, but providing the infrastructure support for a large class to learn...
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...