Papers with feature auto-encoder loss component

1 papers
Evaluating the Utility of Hand-crafted Features in Sequence Labelling (D18-1)

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Challenge: Conventional wisdom is that hand-crafted features are redundant for deep learning models . authors propose a method for using handcrafted features in a hybrid learning approach .
Approach: They propose a method for exploiting handcrafted features as part of a hybrid learning approach.
Outcome: The proposed method outperforms baseline models on a named entity recognition task and reduces training requirements to 60% while maintaining the same predictive accuracy.

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