We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to...
Pedro Quelhas, Florent Monay, Jean-Marc Odobez, Da...
This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data...
Carla Scalarin, Jacques Masse, Jean-Marc Boucher, ...
Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data...
Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, Ne...
Abstracts "Mixtures at the Interface" David Scott, Rice University Mixture modeling provides an effective framework for complex, high-dimensional data. The potential of m...
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...