Back
We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future.
Bogdan Savchynskyy (Computer Vision Lab Dresden)
Bogdan Savchynskyy is a senior researcher in TU Dresden. His main research interests are optimization problems in computer vision and machine learning. In particular, he is an author of a number of papers on exact and approximate inference for discrete graphical models. One of his recent works in this field has got an award at CVPR 14 conference.