The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...
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...
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
Compared to subthreshold leakage, dynamic power is normally much less sensitive to the process variation due to its approximately linear relation to the process parameters. Howeve...
We describe algorithms to explore the space of all possible reconciliations between a gene tree and a species tree. We propose an algorithm for generating a random reconciliation, ...