
The paper proposes a novel framework called Explicit Loss Embedding (ELE) that leverages contrastive learning to learn differentiable surrogate losses for structured prediction, improving performance and enabling the prediction of new structures.


coremsg

learning-differentiable-surrogate-losses-for-structured-prediction-using-contrastive-learning-and-explicit-loss-embedding


Learning Differentiable Surrogate Losses for Structured Prediction Using Contrastive Learning and Explicit Loss Embedding
