1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…
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…
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…
LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains
Ibna Kowsar, Kazi F. Akhter, Manar D. Samad
Transfer learning on tabular data is challenging due to disparate feature spaces across domains, in contrast to the homogeneous structures of image and text. Large language models…
Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
Manar D. Samad, Kazi Fuad B. Akhter, Shourav B. Rabbani +1
Tabular data sets with varying missing values are prepared for machine learning using an arbitrary imputation strategy. Synthetic values generated by imputation models often raise…
Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM
Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque +2
The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, includ…