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. 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.

2022: Bernhard Schafl, Lukas Gruber, Angela Bitto-Nemling, S. Hochreiter

Ranked #1 on General Classification on Shrutime

https://arxiv.org/pdf/2206.00664v1.pdf