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Error Autocorrelation Objective Function for Improved System Modeling
[article]
2021
arXiv
pre-print
Deep learning models are trained to minimize the error between the model's output and the actual values. The typical cost function, the Mean Squared Error (MSE), arises from maximizing the log-likelihood of additive independent, identically distributed Gaussian noise. However, minimizing MSE fails to minimize the residuals' cross-correlations, leading to over-fitting and poor extrapolation of the model outside the training set (generalization). In this paper, we introduce a "whitening" cost
arXiv:2008.03582v2
fatcat:6ha7l7wmzbfpnjoote7zuvghzu