activity
20232026
most citedMultiview learning with twin parametric margin SVM

21 citations · 51 across the 26 of their papers we have counts for

collaborators
Showing cs.LGShow all

26 papers · 1 filter

cs.LG2026

IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss

Mushir Akhtar, M. Tanveer

Broad Learning System is an efficient randomized learning model that expands network width through feature and enhancement nodes and estimates the output weights without deep backp…

cs.LG2026

Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty

Mushir Akhtar, A. Varshney, A. Quadir +3

Broad Learning System (BLS) offers an efficient alternative to deep architectures by enabling fast learning through randomized feature mapping and closed-form solutions. However, i…

cs.LG2026

ECA-BLS: An Efficient Complex-Augmented Broad Learning System

A. Rahaman, A. Quadir, M. Sajid +2

Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization under limited data. However,…

cs.LG2026

GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis

M. Tanveer, Ayush Singh Rana, Sanskriti Jain +5

Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods are limited by small and im…

cs.LG2026

RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network

A. Rahaman, A. Quadir, M. Tanveer

The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical min…

cs.LG2026

Robust Dual-Model Collaborative Random Vector Functional Link Network

A. Quadir, A. Rahaman, Mushir Akhtar +1

Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights…