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Uncertainty Estimation in Autoregressive Structured Prediction
[article]
2021
arXiv
pre-print
Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for autoregressive structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for
arXiv:2002.07650v5
fatcat:eq2i2iqbv5h2di2frxuiaipzg4