Regression with Uncertainty Quantification in Large Scale Complex Data
Nicholas Wilkins, Michael Johnson, Ifeoma Nwogu · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
While several methods for predicting uncertainty on deep networks have been recently proposed, they do not always readily translate to large and complex datasets without significant overhead. In this paper we utilize a special instance of the Mixture Density Networks (MDNs) to produce an elegant and compact approach to quantity uncertainty in regression problems. 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 demonstrate the efficacy and practical usefulness of the method for (i) predicting future stock prices from stochastic, highly volatile time-series data; (ii) anomaly detection in real-life highly complex video segments; and (iii) the task of age estimation and data cleansing on the challenging IMDb-Wiki dataset of half a million face images.