A multi-schematic classifier-independent oversampling approach for imbalanced datasets. - Saptarshi Bej; Kristian Schultz; Prashant Srivastava; Markus Wolfien; Olaf Wolkenhauer (CoRR, 2021)

Attention Retrieval Model for Entity Relation Extraction From Biological Literature. - Prashant Srivastava; Saptarshi Bej; Kristian Schultz; Kristina Y. Yordanova; Olaf Wolkenhauer (IEEE Access, 2022)

Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets. - Kristian Schultz; Saptarshi Bej; Waldemar Hahn; Markus Wolfien; Prashant Srivastava; Olaf Wolkenhauer (CoRR, 2022)

Convex space learning for tabular synthetic data generation. - Manjunath Mahendra; Chaithra Umesh; Saptarshi Bej; Kristian Schultz; Olaf Wolkenhauer (CoRR, 2024)

ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. - Kristian Schultz; Saptarshi Bej; Waldemar Hahn; Markus Wolfien; Prashant Srivastava; Olaf Wolkenhauer (Pattern Recognit., 2024)

Bitter peptide prediction using graph neural networks. - Prashant Srivastava; Alexandra Steuer; Francesco Ferri; Alessandro Nicoli; Kristian Schultz; Saptarshi Bej; Antonella Di Pizio; Olaf Wolkenhauer (J. Cheminformatics, 2024)

Preserving logical and functional dependencies in synthetic tabular data. - Chaithra Umesh; Kristian Schultz; Manjunath Mahendra; Saptarshi Bej; Olaf Wolkenhauer (Pattern Recognit., 2025)

Dependency-aware synthetic tabular data generation. - Chaithra Umesh; Kristian Schultz; Manjunath Mahendra; Saptarshi Bej; Olaf Wolkenhauer (CoRR, 2025)