activity
20212026
most citedA hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

55 citations · 179 across the 23 of their papers we have counts for

collaborators

24 papers

cs.LG2026

RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection

Taufikur Rahman Fuad, Md Abrar Jahin, Amir Hussain

Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-derived notions of normality un…

cs.LG2026

ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs

Md Abrar Jahin, Taufikur Rahman Fuad, Jay Pujara +1

Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade-off: attention-based methods of…

cs.CV2025

DyCAF-Net: Dynamic Class-Aware Fusion Network

Md Abrar Jahin, Shahriar Soudeep, M. F. Mridha +2

Recent advancements in object detection rely on modular architectures with multi-scale fusion and attention mechanisms. However, static fusion heuristics and class-agnostic attenti…

cs.LG2025

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Md Abrar Jahin, Taufikur Rahman Fuad, M. F. Mridha +2

Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environmen…

cs.LG2025

Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems

Md Abrar Jahin, Shahriar Soudeep, M. F. Mridha +2

Real-time particle transverse momentum () estimation in high-energy physics demands algorithms that are both efficient and accurate under strict hardware constraints. Static m…

cs.LG2025

Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems

Md Abrar Jahin, Adiba Abid, M. F. Mridha

Expert systems often operate in domains characterized by class-imbalanced tabular data, where detecting rare but critical instances is essential for safety and reliability. While c…