  While Deep Learning excels in structured data as encountered in vision and
natural language processing, it failed to meet its expectations on tabular
data. For tabular data, Support Vector Machines (SVMs), Random Forests, and
Gradient Boosting are the best performing techniques with Gradient Boosting in
the lead. Recently, we saw a surge of Deep Learning methods that were tailored
to tabular data but still underperform compared to Gradient Boosting on
small-sized datasets. We suggest "Hopular", a novel Deep Learning architecture
for medium- and small-sized datasets, where each layer is equipped with
continuous modern Hopfield networks. The modern Hopfield networks use stored
data to identify feature-feature, feature-target, and sample-sample
dependencies. Hopular's novelty is that every layer can directly access the
original input as well as the whole training set via stored data in the
Hopfield networks. Therefore, Hopular can step-wise update its current model
and the resulting prediction at every layer like standard iterative learning
algorithms. In experiments on small-sized tabular datasets with less than 1,000
samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in
particular several Deep Learning methods. In experiments on medium-sized
tabular data with about 10,000 samples, Hopular outperforms XGBoost, CatBoost,
LightGBM and a state-of-the art Deep Learning method designed for tabular data.
Thus, Hopular is a strong alternative to these methods on tabular data.
