840 research outputs found
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
We apply stochastic average gradient (SAG) algorithms for training
conditional random fields (CRFs). We describe a practical implementation that
uses structure in the CRF gradient to reduce the memory requirement of this
linearly-convergent stochastic gradient method, propose a non-uniform sampling
scheme that substantially improves practical performance, and analyze the rate
of convergence of the SAGA variant under non-uniform sampling. Our experimental
results reveal that our method often significantly outperforms existing methods
in terms of the training objective, and performs as well or better than
optimally-tuned stochastic gradient methods in terms of test error.Comment: AI/Stats 2015, 24 page
- …