Gradient-based implementation of linear model outperforms deep learning models
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.29.551062v1?rss=1

Authors: Hamidi, H., Wang, D., Long, Q., Greenberg, M.

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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