This paper presents varifold learning, a learning framework based on the mathematical concept of varifolds. Different from manifold based methods, our varifold learning framework ...
This paper proposes an organized generalization of Newman and Girvan’s modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure prod...
A basic understanding of the relationship between activity of individual neurons and macroscopic electrical activity of local field potentials or electroencephalogram (EEG) may pro...
Jennifer Dwyer, Hyong Lee, Amber Martell, Rick L. ...
We present an EM-algorithm for the problem of learning preferences with Gaussian processes in the context of multi-task learning. We validate our approach on an audiological data ...
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensiona...
We introduce a new causal hierarchical belief network for image segmentation. Contrary to classical tree structured (or pyramidal) models, the factor graph of the network contains...