5 papers · 1 filter
BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali +2
High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by , where = number of samples, and = number of features. Such domains oft…
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Kumar Gyawali +2
We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones. We introdu…
DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data
Al Zadid Sultan Bin Habib, Gianfranco Doretto, Donald A. Adjeroh
High-dimensional tabular data lacks a natural feature order, limiting the applicability of permutation-sensitive deep learning models. We propose DynaTab, a dynamic feature orderin…
GUESS: Generative Uncertainty Ensemble for Self Supervision
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self-supervised learning (SSL) frameworks consist of pretext task, and loss function aiming to learn useful general features from unlabeled data. The basic idea of most SSL baselin…
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering
Al Zadid Sultan Bin Habib, Kesheng Wang, Mary-Anne Hartley +2
Effective analysis of tabular data still poses a significant problem in deep learning, mainly because features in tabular datasets are often heterogeneous and have different levels…