7 citations · 9 across the 4 of their papers we have counts for
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stat.ML2023★ 7 cited
Temporal Graph Neural Networks for Irregular Data
Joel Oskarsson, Per Sidén, Fredrik Lindsten
This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregula…
stat.ML2022★ 2 cited
Scalable Deep Gaussian Markov Random Fields for General Graphs
Joel Oskarsson, Per Sidén, Fredrik Lindsten
Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields…