research
∙
08/04/2022
Node Copying: A Random Graph Model for Effective Graph Sampling
There has been an increased interest in applying machine learning techni...
share
research
∙
02/22/2022
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Multiple Instance Learning (MIL) is a weakly supervised learning problem...
research
∙
06/10/2021
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wirel...
research
∙
07/09/2020
Node Copying for Protection Against Graph Neural Network Topology Attacks
Adversarial attacks can affect the performance of existing deep learning...
research
∙
07/09/2020
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Node classification in attributed graphs is an important task in multipl...
research
∙
06/23/2020
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Graphs are ubiquitous in modelling relational structures. Recent endeavo...
research
∙
11/08/2019
Bayesian Graph Convolutional Neural Networks using Node Copying
Graph convolutional neural networks (GCNN) have numerous applications in...
research
∙
10/26/2019
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Graph convolutional neural networks (GCNN) have been successfully applie...
research
∙
11/27/2018
Bayesian graph convolutional neural networks for semi-supervised classification
Recently, techniques for applying convolutional neural networks to graph...
research
∙
07/06/2018
