The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Search spaces sampled by the process of Genetic Programming often consist of programs which can represent a function in many different ways. Thus, when the space is examined it i...
The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should...
We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model ...
Modern processors rely on memory dependence prediction to execute load instructions as early as possible, speculating that they are not dependent on an earlier, unissued store. To...
Franziska Roesner, Doug Burger, Stephen W. Keckler