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

arXiv:2403.15790v1 Announce Type: new
Abstract: 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 this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders. Autoencoders are widely employed for learning and constructing a new representation of a dataset, particularly for dimensionality reduction. …

abstract arxiv autoencoder challenges context cs.lg data datasets discovery gap image image datasets iss mixed paper research self-supervised learning stat.ml supervised learning tabular tabular data type