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20192026
most citedInformation Mining for COVID-19 Research From a Large Volume of Scientific Literature

15 citations · 33 across the 21 of their papers we have counts for

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8 papers · 1 filter

stat.ML2026

Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data

Heranga K. Rathnasekara, Norou Diawara, Manar D. Samad

Diagnostic errors and mislabeling are common in biomedicine, which compromise the reliability of predictive models and data-driven outcomes. Stratifying unlabeled biomedical data b…

cs.LG2026

Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data

Kazi F. Akhter, Ibna Kowsar, Manar D. Samad

Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics across disparate domains. Existin…

cs.LG2026

Importance Scoring of Transformer Attention Heads in Learning Tabular Data

Ahmad Jad Allah, Kazi F. Akhter, Md. Kamrozzaman Bhuiyan +1

Computationally demanding and opaque deep learning models can be better understood and optimized by analyzing how they transform data. While deep transformers have been widely stud…

cs.LG2026

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1

Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy. Unsuper…

cs.DB2026

A Fair Benchmarking of Deep Relational Database Learning Models

Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent exper…

stat.CO2026

Vine Copulas for Analyzing Multivariate Conditional Dependencies in Electronic Health Records Data

Manar D. Samad, Yina Hou, Megan A. Witherow +1

Electronic health records (EHR) store hundreds of demographic and laboratory variables from large patient populations. Traditional statistical methods have limited capacity in proc…