33 citations · 145 across the 50 of their papers we have counts for
52 papers
Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment
Keren Artiaga, Yang Li, Ercan Engin Kuruoglu +2
Sign language serves as a vital means of communication for individuals with hearing impairments, yet recognition resources for the over 100 distinct sign languages are severely lac…
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
Yihan Zhang, Ercan E. Kuruoglu
Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and labels interact in diverse…
Function-Space Empirical Bayes Regularisation with Student's t Priors
Pengcheng Hao, Ercan Engin Kuruoglu
Bayesian deep learning (BDL) has emerged as a principled approach to produce reliable uncertainty estimates by integrating deep neural networks with Bayesian inference, and the sel…
Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors
Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu +1
Bayesian deep learning (BDL) provides a principled framework for reliable uncertainty quantification by combining deep neural networks with Bayesian inference. A central challenge…
Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
Yihan Zhang, Ercan E. Kuruoglu
Multimodal graphs are gaining increasing attention due to their rich representational power and wide applicability, yet they introduce substantial challenges arising from severe mo…
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu +5
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the ove…
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
Hanbing Liu, Yanru Wu, Yang Li +2
Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping…
Monte Carlo Functional Regularisation for Continual Learning
Pengcheng Hao, Menghao Waiyan William Zhu, Ercan Engin Kuruoglu
Continual learning (CL) is crucial for the adaptation of neural network models to new environments. Although outperforming weight-space regularisation approaches, the functional re…
BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification
Jiacheng Hou, Zhenjie Song, Ercan Engin Kuruoglu
Recent studies have made great progress in functional brain network classification by modeling the brain as a network of Regions of Interest (ROIs) and leveraging their connections…
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu
Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication and social interaction. Accurate a…
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu
Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classi…
Spatio-Temporal Graph Structure Learning for Earthquake Detection
Suchanun Piriyasatit, Ercan Engin Kuruoglu, Mehmet Sinan Ozeren
Earthquake detection is essential for earthquake early warning (EEW) systems. Traditional methods struggle with low signal-to-noise ratios and single-station reliance, limiting the…
Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
Menghao Waiyan William Zhu, Pengcheng Hao, Ercan Engin Kuruoğlu
Continual learning in neural networks aims to learn new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) uses a Gaussian variational dist…
A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu +6
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-sm…
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
Xueqiong Yuan, Jipeng Li, Ercan Engin Kuruoglu
Model uncertainty quantification involves measuring and evaluating the uncertainty linked to a model's predictions, helping assess their reliability and confidence. Noise injection…
Signal Processing over Time-Varying Graphs: A Systematic Review
Yi Yan, Jiacheng Hou, Zhenjie Song +1
As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such a…
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…
Topological and Graph Theoretical Analysis of Dynamic Functional Connectivity for Autism Spectrum Disorder
Yuzhe Chen, Dayu Qin, Ercan Engin Kuruoglu
Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD patients are hindering the diagn…
LLM-based Online Prediction of Time-varying Graph Signals
Dayu Qin, Yi Yan, Ercan Engin Kuruoglu
In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and tempor…
Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder: A Thermodynamics Parameters Analysis Approach
Dayu Qin, Yuzhe Chen, Ercan Engin Kuruoglu
Designing network parameters that can effectively represent complex networks is of significant importance for the analysis of time-varying complex networks. This paper introduces a…
Adaptive Graph Normalized Sign Algorithm
Changran Peng, Yi Yan, Ercan E. Kuruoglu
Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data. Exploiting graph signal processing (GSP) and lever…
On Sequential Maximum a Posteriori Inference for Continual Learning
Menghao Waiyan William Zhu, Ercan Engin Kuruoğlu
We formulate sequential maximum a posteriori inference as a recursion of loss functions and reduce the problem of continual learning to approximating the previous loss function. We…