Search Machine Learning Repository:
Putting MRFs on a Tensor Train
Authors: Alexander Novikov, Anton Rodomanov, Anton Osokin and Dmitry Vetrov
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: In the paper we present a new framework for dealing with probabilistic graphical models. Our approach relies on the recently proposed Tensor Train format (TT-format) of a tensor that while being compact allows for efficient application of linear algebra operations. We present a way to convert the energy of a Markov random field to the TT-format and show how one can exploit the properties of the TT-format to attack the tasks of the partition function estimation and the MAP-inference. We provide theoretical guarantees on the accuracy of the proposed algorithm for estimating the partition function and compare our methods against several state-of-the-art algorithms.
authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).