Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models

@article{Ravichander2017PreservingIO,
  title={Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models},
  author={Abhilasha Ravichander and Shruti Rijhwani and Rajat Kulshreshtha and Chirag Nagpal and Tadas Baltrusaitis and Louis-Philippe Morency},
  journal={CoRR},
  year={2017},
  volume={abs/1706.07867}
}
Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a final task. Typically, neural networks are first trained for the subtasks, and the predictions of these networks are subsequently used as additional features when training a model and doing inference for a final task. In this work, we focus on improving learning for such hierarchical models and demonstrate our method… CONTINUE READING

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