• Corpus ID: 2232524

SpeedMachines: Anytime Structured Prediction

@article{Grubb2013SpeedMachinesAS,
  title={SpeedMachines: Anytime Structured Prediction},
  author={Alexander Grubb and Daniel Munoz and J. Andrew Bagnell and Martial Hebert},
  journal={ArXiv},
  year={2013},
  volume={abs/1312.0579}
}
Structured prediction plays a central role in machine learning applications from computational biology to computer vision. These models require significantly more computation than unstructured models, and, in many applications, algorithms may need to make predictions within a computational budget or in an anytime fashion. In this work we propose an anytime technique for learning structured prediction that, at training time, incorporates both structural elements and feature computation trade… 

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