Gradient-based implementation of linear model outperforms deep learning models
Hamidi, H.; Wang, D.; Long, Q.; Greenberg, M.
Show abstract
Deep learning has been widely considered more effective than traditional statistical models in modeling biological complex data such as single-cell omics. Here we show the devil is hidden in details: by adapting a modern gradient solver to a traditional linear mixed model, we showed that conventional models can outperform deep models in terms of both speed and accuracy. This work reveals the potential of re-implementing traditional models with modern solvers.
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