3 citations · 3 across the 2 of their papers we have counts for
10 papers · 1 filter
Bayesian Neural Networks: A Min-Max Game Framework
Junping Hong, Ercan Engin Kuruoglu
In deep learning, Bayesian neural networks (BNN) provide the role of robustness analysis, and the minimax method is used to be a conservative choice in the traditional Bayesian fie…
Graph Frequency Features of Cancer Gene Co-Expression Networks
Radwa Adel, Ercan Engin Kuruoglu
Complex gene interactions play a significant role in cancer progression, driving cellular behaviors that contribute to tumor growth, invasion, and metastasis. Gene co-expression ne…
LLM Online Spatial-temporal Signal Reconstruction Under Noise
Yi Yan, Dayu Qin, Ercan Engin Kuruoglu
This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online…
Graph Signal Adaptive Message Passing
Yi Yan, Changran Peng, Ercan Engin Kuruoglu
This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise…
Time-varying Graph Signal Estimation via Dynamic Multi-hop Topologies
Yi Yan, Fengfan Zhao, Ercan Engin Kuruoglu
The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity of modeling their interactions over time. We propose a Dynamic M…
Adaptive Least Mean pth Power Graph Neural Networks
Yi Yan, Changran Peng, Ercan E. Kuruoglu
In the presence of impulsive noise, and missing observations, accurate online prediction of time-varying graph signals poses a crucial challenge in numerous application domains. We…