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pro vyhledávání: '"Figeuiredo, Mario A. T."'
Autor:
Martins, Andre F.T., Smith, Noah A., Xing, Eric P., Aguiar, Pedro M.Q., Figeuiredo, Mario A. T.
Training structured predictors often requires a considerable time selecting features or tweaking the kernel. Multiple kernel learning (MKL) sidesteps this issue by embedding the kernel learning into the training procedure. Despite the recent progress
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::377da0411cb9e8de29563d5e396d57fa
Autor:
Martins, Andre F.T., Figeuiredo, Mario A. T., Aguiar, Pedro M.Q., Smith, Noah A., Xing, Eric P
We propose AD3 , a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of multipliers. Like dual decomposition algorithms, AD3 uses worker nodes to iteratively solve local sub
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7f171b0acab81c648f8e0fcc57a8a15
Autor:
Martins, Andre F.T., Figeuiredo, Mario A. T., Aguiar, Pedro M.Q., Smith, Noah A., Xing, Eric P
We propose a new fast algorithm for approximate MAP inference on factor graphs, which combines augmented Lagrangian optimization with the dual decomposition method. Each slave subproblem is given a quadratic penalty, which pushes toward faster consen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76e191bbcb60af71719061096f064f63