33 citations · 145 across the 50 of their papers we have counts for
23 papers · 1 filter
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…
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…
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…
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…
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…
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…
Binarized Simplicial Convolutional Neural Networks
Yi Yan, Ercan E. Kuruoglu
Graph Neural Networks have a limitation of solely processing features on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolu…
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
Mengen Luo, Chi Xu, Ercan Engin Kuruoglu
Performance degradation owing to data heterogeneity and low output interpretability are the most significant challenges faced by federated learning in practical applications. Perso…
Bayesian Neural Network For Personalized Federated Learning Parameter Selection
Mengen Luo, Ercan Engin Kuruoglu
Federated learning's poor performance in the presence of heterogeneous data remains one of the most pressing issues in the field. Personalized federated learning departs from the c…
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
Yi Yan, Changran Peng, Ercan Engin Kuruoglu
The online prediction of multivariate signals, existing simultaneously in space and time, from noisy partial observations is a fundamental task in numerous applications. We propose…
Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise
Xueqiong Yuan, Jipeng Li, Ercan Engin Kuruoğlu
With the increasing use of deep learning on data collected by non-perfect sensors and in non-perfect environments, the robustness of deep learning systems has become an important i…
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…
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information
Zhendong Shi, Xiaoli Wei, Ercan E. Kuruoglu
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in ma…
PAC-Bayes Information Bottleneck
Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu +3
Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works tryi…
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Zifeng Wang, Xi Chen, Rui Wen +3
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommen…
Stable Graphical Models
Navodit Misra, Ercan E. Kuruoglu
Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gauss…