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Sparsification for CRF transition matrix #982
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Summary: Pull Request resolved: facebookresearch#982 1. added sparsification support for CRF. Two sparsification methods are supported a) l1-regularization via soft-thresholding method to update the transition matrix b) magnitude-based, which keeps top elements in each column of the transition matrix until sparsity is met. 2. small refactoring on how sparsifier is called in trainer.py. Sparsifier in effect only after a step if sparsification_condition returns true. Differential Revision: D17408540 fbshipit-source-id: 482607c0b23af12cab53d58c7361d3ef6eb5b6d8
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Summary: Pull Request resolved: facebookresearch#982 1. added sparsification support for CRF. Two sparsification methods are supported a) l1-regularization via soft-thresholding method to update the transition matrix b) magnitude-based, which keeps top elements in each column of the transition matrix until sparsity is met. 2. small refactoring on how sparsifier is called in trainer.py. Sparsifier in effect only after a step if sparsification_condition returns true. Differential Revision: D17408540 fbshipit-source-id: 51841aa6312c6b78c85979d050e0fed02363129f
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Summary: Pull Request resolved: facebookresearch#982 1. added sparsification support for CRF. Two sparsification methods are supported a) l1-regularization via soft-thresholding method to update the transition matrix b) magnitude-based, which keeps top elements in each column of the transition matrix until sparsity is met. 2. small refactoring on how sparsifier is called in trainer.py. Sparsifier in effect only after a step if sparsification_condition returns true. Differential Revision: D17408540 fbshipit-source-id: db2f6dadbe300ea979cf1d0166fa6556c6a50c39
Summary: Pull Request resolved: facebookresearch#982 1. added sparsification support for CRF. Two sparsification methods are supported a) l1-regularization via soft-thresholding method to update the transition matrix b) magnitude-based, which keeps top elements in each column of the transition matrix until sparsity is met. 2. small refactoring on how sparsifier is called in trainer.py. Sparsifier in effect only after a step if sparsification_condition returns true. Reviewed By: arbabu123 Differential Revision: D17408540 fbshipit-source-id: c7a448b68f21fe9759971558bc88f21159ca7485
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Summary:
added sparsification support for CRF. Two sparsification methods are supported a) l1-regularization via soft-thresholding method to update the transition matrix
b) magnitude-based, which keeps top elements in each column of the transition matrix until sparsity is met.
small refactoring on how sparsifier is called in trainer.py. Sparsifier in effect only after a step if sparsification_condition returns true.
Differential Revision: D17408540