10 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals
Shoukun Lyu, Haohan Sun, Shibo Nie +5
Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory a…
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…
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
Sania Eskandari, Ali Eslamian, Nusrat Munia +2
This study evaluates the effectiveness of deep learning models in classifying histopathological images for early and accurate detection of breast cancer. Eight advanced models, inc…