20 Jan 2020

# Uncertainty in Deep Learning

This post is my learning notes for paper

# 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