Description

Implementation of the paper Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Predictive uncertainty estimates obtained by using the ensemble approach proposed in the paper. The value of the sinusoidal function for values between -4 and 4 was used in training and at test time, the trained model is used to predict function values between -8 and 8. The blue line represents the true function, red represents the mean and the other two curves represent (mean + 3*std)

Predictive uncertainty estimates obtained by using a dropout Gaussian regressor. During training, we train the model to minimize NLL with dropout. Hence, as a result, a larger ensemble of networks are trained simultaneously. At test time, we maintain the same dropout and average the predictions of a fixed number (in the above plot, 50) of networks that are part of the trained ensemble.

I also tried out the kink example given in the paper by breaking the cubic curve in the middle and introducing a kink (sinusoidal curve)

The implementation is partly inspired from this repository

Author : Anirudh Vemula