Saptarshi Bej

According to our database, Saptarshi Bej authored at least 26 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

Correction: Multivariate functional linear discriminant analysis for partially‑observed time series.

Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer

Mach. Learn., July, 2026

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision.

Sujan Chakraborty, Saptarshi Bej

CoRR, May, 2026

Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement.

Pritam Kar, Gouri Lakshmi, Saptarshi Bej

CoRR, April, 2026

Anomaly Detection via Mean Shift Density Enhancement.

Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej

CoRR, February, 2026

Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks.

Gouri Lakshmi, Athira Chandrasekharan, Harshit Kumar, Muhammed Sahad E, Bikas C. Das, Saptarshi Bej

CoRR, January, 2026

Dependency-aware synthetic tabular data generation.

Chaithra Umesh, Kristian Schultz, Manjunath Mahendra, Saptarshi Bej, Olaf Wolkenhauer

Pattern Recognit., 2026

Fast agreement-driven device-calibrated local learning paradigms for spiking neural networks.

Saptarshi Bej, Muhammed Sahad E, Gouri Lakshmi, Harshit Kumar, Pritam Kar, Bikas C. Das

Neural Networks, 2026

Convex space learning for tabular synthetic data generation.

Manjunath Mahendra, Chaithra Umesh, Kristian Schultz, Olaf Wolkenhauer, Saptarshi Bej

Neurocomputing, 2026

Multivariate Functional Linear Discriminant Analysis for Partially-Observed Time Series (Abstract Reprint).

Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization.

Sujan Chakraborty, Rahul Bordoloi, Anindya Sengupta, Olaf Wolkenhauer, Saptarshi Bej

CoRR, October, 2025

Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks.

Saptarshi Bej, Muhammed Sahad E, Gouri Lakshmi, Harshit Kumar, Pritam Kar, Bikas C. Das

CoRR, August, 2025

Multivariate functional linear discriminant analysis for partially-observed time series.

Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer

Mach. Learn., March, 2025

Fast Iterative and Task-Specific Imputation with Online Learning.

Rahul Bordoloi, Clémence Réda, Saptarshi Bej

CoRR, January, 2025

Preserving logical and functional dependencies in synthetic tabular data.

Chaithra Umesh, Kristian Schultz, Manjunath Mahendra, Saptarshi Bej, Olaf Wolkenhauer

Pattern Recognit., 2025

Detection of pre-ictal epileptic events using a self-attention based neural network from raw Neonatal EEG data.

Kondaveeti Tejaswi, Madala Vikas, Himala Praharsha, Pranshu Mandal, Sujan Chakraborty, Olaf Wolkenhauer, Saptarshi Bej

Comput. Biol. Medicine, 2025

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, December, 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., March, 2024

Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data.

Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer

CoRR, 2024

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

Attention Retrieval Model for Entity Relation Extraction From Biological Literature.

Prashant Srivastava, Saptarshi Bej, Kristian Schultz, Kristina Y. Yordanova, Olaf Wolkenhauer

IEEE Access, 2022

2021

Improved imbalanced classification through convex space learning.

Saptarshi Bej

PhD thesis, 2021

LoRAS: an oversampling approach for imbalanced datasets.

Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer

Mach. Learn., 2021

A multi-schematic classifier-independent oversampling approach for imbalanced datasets.

Saptarshi Bej, Kristian Schultz, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer

CoRR, 2021

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling.

Saptarshi Bej, Anne-Marie Galow, Robert David, Markus Wolfien, Olaf Wolkenhauer

BMC Bioinform., 2021

A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets.

Saptarshi Bej, Kristian Schulz, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer

IEEE Access, 2021

Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets.

Saptarshi Bej, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer

Proceedings of the International Joint Conference on Neural Networks, 2021