1 citations · 2 across the 7 of their papers we have counts for
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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…
Transfer Learning of Tabular Data by Finetuning Large Language Models
Shourav B. Rabbani, Ibna Kowsar, Manar D. Samad
Despite the artificial intelligence (AI) revolution, deep learning has yet to achieve much success with tabular data due to heterogeneous feature space and limited sample sizes wit…
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework
Ibna Kowsar, Shourav B. Rabbani, Yina Hou +1
Missing values of varying patterns and rates in real-world tabular data pose a significant challenge in developing reliable data-driven models. The most commonly used statistical a…
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad
Despite groundbreaking success in image and text learning, deep learning has not achieved significant improvements against traditional machine learning (ML) when it comes to tabula…
Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks
Shourav B. Rabbani, Manar D. Samad
Traditional machine learning assumes samples in tabular data to be independent and identically distributed (i.i.d). This assumption may miss useful information within and between s…
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad
Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneou…