A Smoother Way to Train Structured Prediction Models

NeurIPS 2018 Krishna PillutlaVincent RouletSham M. KakadeZaid Harchaoui

We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to the maximum margin structured prediction objective, and paves the way for the use of fast primal gradient-based optimization algorithms... (read more)

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