10 citations · 12 across the 5 of their papers we have counts for
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cs.LG2025★ 1 cited
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network
Ali Eslamian, Alireza Afzal Aghaei, Qiang Cheng
Tabular data analysis presents unique challenges that arise from heterogeneous feature types, missing values, and complex feature interactions. While traditional machine learning m…
cs.LG2025★ 1 cited
TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
Ali Eslamian, Qiang Cheng
Tabular data poses unique challenges for deep learning due to its heterogeneous feature types, lack of spatial structure, and often limited sample sizes. We propose TabNSA, a novel…