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Zehui Chen

20 Jan 2020

Uncertainty in Deep Learning

This post is my learning notes for paper </a> by **YARIN GAL**.

Chapter1 Introduction: The Importance of Knowing What We Don’t Know

Model Uncertainty

  • When encountering out of distribution data, model should yield out high uncertainty, conveying low confidence)
  • noisy data
  • Uncertainty in model parameters that best explain the observed data((a large number of possible models might be able to explain a given dataset, in which case we might be uncertain which model parameters to choose to predict with)
  • structure uncertainty(what model structure should we use? how do we specify our model to extrapolate / interpolate well?)

End of Post
at 02:57

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