Yoel Zeldes • 7/31/2018

Using Uncertainty to Interpret your Model

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This technical article discusses the importance of model interpretability in complex deep neural networks. It focuses on using uncertainty estimation methods to debug models, handle out-of-distribution examples, and improve decision-making in high-risk applications like healthcare and autonomous vehicles. The post covers different types of uncertainty (epistemic) and their practical uses for practitioners.

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#Machine Learning #Deep Neural Networks #Model Interpretability
Using Uncertainty to Interpret your Model

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