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To have a superior generalization, a deep learning neural network ofteninvolves a large size of training sample. With increase of hidden layers inorder to increase learning ability, neural network has potential degradation inaccuracy. Both could seriously limit applicability of deep learning in somedomains particularly involving predictions of continuous variables with a smallsize of samples. Inspired by residual convolutional neural network in computervision and recent findings of crucial shortcuts in the brains in neuroscience,we propose an autoencoder-based residual deep network for robust prediction. Ina nested way, we leverage shortcut connections to implement residual mappingwith a balanced structure for efficient propagation of error signals. The novelmethod is demonstrated by multiple datasets, imputation of high spatiotemporalresolution non-randomness missing values of aerosol optical depth, andspatiotemporal estimation of fine particulate matter <2.5 \mu m, achieving thecutting edge of accuracy and efficiency. Our approach is also a general-purposeregression learner to be applicable in diverse domains.

Autoencoder Based Residual Deep Networks for Robust Regression Prediction and Spatiotemporal Estimation