arXiv subjects

Kazi F. Akhter

Publications and source records attributed to Kazi F. Akhter.

4 recordsLinked to original sources

arXiv2026

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

Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics across disparate domains. Existing methods assume shared features across data tables to enable knowledge transfer between domains, which is unrealistic in practice. \mds{This paper introduces generalized context learning to remove the requirement of shared features across domains. The generalized context captured by transformer projection weights for $key$, $value$, and $query$ provides rule-based generalization rather than the domain-specific context conventionally learned from transformer activations. Projection weights for $key$ from the source domain interact with the weight for $query$ in the target domain to achieve Cross-domain Attention Transfer Learning (CATTLE) in a data-agnostic manner. Our experiments on ten pairs of disjoint source-target data sets show that CATTLE can learn generalized context from a single source data set and is rank-wise and statistically superior to nine state-of-the-art baselines, including machine learning, deep learning, and transfer learning methods using large-scale pre-trained models. CATTLE achieves the best average rank (2.9) and delivers a 3.7% average AUROC gain over the baseline methods.} The CATTLE source code is available at https://tinyurl.com/pr5s8ywn.

cs.LG↗

arXiv2026

Importance Scoring of Transformer Attention Heads in Learning Tabular Data

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 studied in computer vision and natural language processing, their application in tabular data remains relatively underexplored. This paper presents one of the first applications of an importance-scoring metric to interpret multi-head transformer models in learning from tabular data. Experiments conducted on 40 diverse tabular datasets demonstrate robustness to head drops based on the proposed head importance score. In 72.5\% of experimental examples, the model remains most resilient to performance drops when heads with the lowest importance scores are gradually removed. In contrast, removing the most important attention head first results in the greatest reduction in classification performance. A closer look at individual head importance scores across six attention layers reveals that important heads are scattered across layers, with no consistent layer-specific trends. In contrast to the image and language domains, the importance of individual attention heads varies considerably across tabular datasets with different schemas and feature spaces. The proposed importance score can improve efficiency and redundancy within transformer architectures. We make the source code for measuring the importance of individual attention heads publicly available.

cs.LG↗

arXiv2026

A Fair Benchmarking of Deep Relational Database Learning Models

Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult. We present one of the first systematic benchmarking studies of recently released deep learning methods for RDBs, evaluating them across five relational databases, with one classification and one regression task for each. We refactor all deep RDB models to allow the full range of experimental procedures to be applied consistently across all methods. Our findings indicate that the relational transformer (RT) approach delivers the strongest overall performance on both classification and regression tasks compared to the state-of-the-art graph-based modeling and learning of RDBs. Even for single-table learning tasks, deep learning methods designed for RDBs outperform the leading tabular foundation model, TabPFN 2.5. Extending learning from a single table (hop = 0) to multiple tables (hop = 1, 2) by connecting neighboring tables in relational databases enhances performance, but the additional benefit from higher hops diminishes as computational overhead grows. Deep RDB learning methods have the potential to challenge state-of-the-art tabular foundation models, especially on large-scale enterprise data. The source code for this benchmarking study is publicly available.

cs.DB↗

arXiv2026

LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains

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 (LLMs) offer a knowledge base to improve the limited effectiveness of cross-domain transfer learning for tabular data. However, LLM performance often stagnates due to subjective text prompts and the computational limitations of in-context learning. We present a novel language-to-tabular context-learning method that uses attention-specific transformer weights, enabling seamless transfer learning across disparate tabular data sets. The LLM attention transplant mechanism facilitates a domain-agnostic transfer learning, eliminating the need for shared features between tables, LLM prompt engineering, and large-scale pretrained models. Our experiments using ten pairs of disjoint source-target data sets and 12 baseline methods demonstrate the superiority of the proposed LLM-attention transplant for transfer learning (LATTLE) method over traditional ML models, state-of-the-art deep tabular architectures, and models trained on thousands to billions of tabular samples. The proposed cross-domain attention transfer demonstrates an effective solution for adapting LLMs to learning non-text tabular data in a low-resource environment. The source code of the LATTLE implementation is publicly available.

cs.LG↗