30 citations · 30 across the 1 of their papers we have counts for
8 papers
Neural Temporal Point Processes: A Review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +1
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with…
Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur, Nicholas Gao, Marin Biloš +1
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, th…
Overlapping Community Detection with Graph Neural Networks
Oleksandr Shchur, Stephan Günnemann
Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community det…
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating t…
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski +1
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on…
Dual-Primal Graph Convolutional Networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski +3
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Grap…