activity
20032026
most citedA Survey of Graph Transformers: Architectures, Theories and Applications

4 citations · 9 across the 26 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.AI20263 cited

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…

cs.AI2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…