A set of conditional bounds shows PERL's advantages follow from assumed smaller Lipschitz constant and loss ceiling, without proving those assumptions or connecting them correctly to neural network training.
Deep-gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning
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Theory Foundation of Physics-Enhanced Residual Learning
A set of conditional bounds shows PERL's advantages follow from assumed smaller Lipschitz constant and loss ceiling, without proving those assumptions or connecting them correctly to neural network training.