1 citations · 1 across the 5 of their papers we have counts for
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Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
Md Sazzad Hossen, Avimanyu Sahoo
Graphs with heterophily, where adjacent nodes carry different labels, are prevalent in real-world applications, from social networks to molecular interactions. However, existing sp…
DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
Atik Faysal, Taha Boushine, Mohammad Rostami +5
We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of mas…
Federated Split Learning for Human Activity Recognition with Differential Privacy
Josue Ndeko, Shaba Shaon, Aubrey Beal +2
This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge…
Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning
Mohammad Rostami, Atik Faysal, Huaxia Wang +1
Overfitting is a significant challenge in Few-Shot Learning (FSL), where models trained on small, variable datasets tend to memorize rather than generalize to unseen tasks. Regular…
Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
Atik Faysal, Mohammad Rostami, Huaxia Wang +2
Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised…