Interdecadal variability in the spatial structure of wind and SST in the California Current system

The physical environment of eastern boundary current systems is rarely uniform in time. ENSO and other perturbations produce profound anomalies in the atmosphere and ocean on interannual to decadal and century time scales. ... The objective of this paper is to describe the temporal variability in the spatial texture of the California Current system, a major eastern boundary current system off the west coast of North America, to provide a base from which to evaluate the effect of climate change - in the recent past, at present, and for the future.

Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.