We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Abstract. Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the...
Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children rec...
David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik
Abstract-- Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper we deal with t...
Anne Benoit, Loris Marchal, Jean-Francois Pineau, ...
Use case diagrams (UCDs) are widely used to describe requirements and desired functionality of software products. However, UCDs are loosely linked to source code, and maintaining ...
Mark Grechanik, Kathryn S. McKinley, Dewayne E. Pe...