  While several methods for predicting uncertainty on deep networks have been
recently proposed, they do not readily translate to large and complex datasets.
In this paper we utilize a simplified form of the Mixture Density Networks
(MDNs) to produce a one-shot approach to quantify uncertainty in regression
problems. We show that our uncertainty bounds are on-par or better than other
reported existing methods. When applied to standard regression benchmark
datasets, we show an improvement in predictive log-likelihood and
root-mean-square-error when compared to existing state-of-the-art methods. We
also demonstrate this method's efficacy on stochastic, highly volatile
time-series data where stock prices are predicted for the next time interval.
The resulting uncertainty graph summarizes significant anomalies in the stock
price chart. Furthermore, we apply this method to the task of age estimation
from the challenging IMDb-Wiki dataset of half a million face images. We
successfully predict the uncertainties associated with the prediction and
empirically analyze the underlying causes of the uncertainties. This
uncertainty quantification can be used to pre-process low quality datasets and
further enable learning.
