Exploiting symmetries for scaling loopy belief propagation and relational training

@article{Ahmadi2013ExploitingSF,
  title={Exploiting symmetries for scaling loopy belief propagation and relational training},
  author={Babak Ahmadi and Kristian Kersting and Martin Mladenov and Sriraam Natarajan},
  journal={Machine Learning},
  year={2013},
  volume={92},
  pages={91-132}
}
Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce… CONTINUE READING

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