Added dropout results

README.md

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 Implementation of the [paper](https://arxiv.org/pdf/1612.01474v1.pdf) **Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles**
 
 ![Sinusoidal gaussian regressor](sinusoidal.png)
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 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)
 
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+![Sinusoidal gaussian dropout regressor](sinusoidal_dropout.png)
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+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 (theoretically, infinite) 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.
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 The implementation is partly inspired from [this](https://github.com/muupan/deep-ensemble-uncertainty) repository
 
 **Author** : Anirudh Vemula