Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Based on scaling laws describing the statistical structure
of turbulent motion across scales, we propose a multiscale
and non-parametric regularizer for optic-flow estimation.
R...
Patrick H´eas, Etienne M´emin, Dominique Heitz, ...
This paper presents a nonparametric Bayesian extension of nonnegative matrix factorization (NMF) for music signal analysis. Instrument sounds often exhibit non-stationary spectral...
Masahiro Nakano, Jonathan Le Roux, Hirokazu Kameok...
A statistical framework for modeling and prediction of binary matrices is presented. The method is applied to social network analysis, specifically the database of US Supreme Cou...
Eric Wang, Jorge Silva, Rebecca Willett, Lawrence ...