Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets

Our paper “Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets,” by Samuel Stocksieker, co-autored with Denys Pommeret, has been accepted for presentation at the IEEE World Congress on Computational Intelligence (IEEE WCCI 2024) to be held at Pacifico Yokohama, Yokohama, Japan, 30 June – 5 July 2024. The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill … <a …