Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its eff...
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
— Recent work has demonstrated the benefits of adopting a fully probabilistic SLAM approach in sequential motion and structure estimation from an image sequence. Unlike standard...
Javier Civera, Andrew J. Davison, J. M. M. Montiel
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...