14 citations · 14 across the 3 of their papers we have counts for
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cs.LG2023
Provably Powerful Graph Neural Networks for Directed Multigraphs
Béni Egressy, Luc von Niederhäusern, Jovan Blanusa +3
This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The…
cs.LG2020
Tabular Transformers for Modeling Multivariate Time Series
Inkit Padhi, Yair Schiff, Igor Melnyk +6
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock t…