We apply stochastic average gradient (SAG)algorithms for training conditional random fields (CRFs).
We describe a practical scheme to reduce the memory requirements of this linear convergence stochastic gradient method by using the structure in the CRF gradient, and propose a significant A non-uniform sampling strategy that improves practical performance, and analyzes the convergence rate of the SAGA mutation algorithm under non-uniform sampling. The experimental results of
We describe a practical implementation thatuses structure in the CRF gradient to reduce the memory requirement of thislinearly-convergent stochastic gradient method, propose a non-uniform samplingscheme that substantially improves practical performance, and analyze the rateof convergence of the SAGA variant under non-uniform sampling.
show that our method is often significantly better than the training objectives of the existing methods, and the performance on the test error is better than the optimally tuned stochastic gradient method.
Our experimental results reveal that ourmethod often significantly outperforms existing methods in terms of thetraining objective, and performs as well or better than optimally-tunedstochastic gradient methods in terms of test error.
Conditional airports (CRFs) are a commonly used tool in natural language processing.
Conditional random fields (CRFs) are aubiquitous tool in natural language processing.
They are used for part-of-speech tagging, semantic role tagging, topic modeling, information extraction, shallow parsing, named entity recognition, and numerous other applications in natural language processing and computer vision.
They are used for part-of-speech tagging ,semantic role labeling , topic modeling , information extraction , shallowparsing , named-entity recognition , as well as a host of other applications innatural language processing and in other fields such as computer vision.
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