Title: Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization Abstract: Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative ... Read more