11 citations · 23 across the 7 of their papers we have counts for
4 papers · 1 filter
TabNSM: Neural Sparse Mixer for Tabular Regression
Ali Eslamian, Qiang Cheng
Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible fe…
Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
Andrew Cheng, Ali Eslamian, Jie Cheng +2
Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen…
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…
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…