Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets…

The characterization of chaos as a random-like response from a deterministic dynamical system with an extreme sensitivity to initial conditions is well-established, and has provided a stimulus to…

Variational analysis has come of age. Long an elegant theoretical toolkit for variational mathematics and nonsmooth optimization, it now increasingly underpins the study of algorithms, and a rich…

CAM Colloquium: Pierre Baldi (UC Irvine) - Deep Architectures and Deep Learning: Theory, Algorithms, and Applications
Friday, March 8, 2013 at 3:30pm
Frank H. T. Rhodes Hall, 655
Deep architectures…