I work at a company with lots of structured data (i.e. rows and columns). I’v been trying for the last year plus to build state-of-the-art deep learning models on a few of these datasets. To be considered state-of-the-art, I generally mean that it would out-perform a gradient-boosting machine implemented via XGBoost or LightGBM. I’ve tried implementations in both keras+TF and now fastai+pytorch but have been unsuccessful to date to find a model that beats gradient-boosted machines.
I’m familiar with a couple kaggle competitions where deep learning has been effective such as Rossman and Porto Seguro. I’ve tried to replicate these solutions on my datasets, but no luck so far. So outside of those examples, can anyone share some success stories of deep learning on structured data? I’m trying to see if these Kaggle anecdotal success stories are harbingers of a deep learning takeover for structured data problems or if they’re outliers relative to the vast number of models being built on structure data all over industry.