27 citations · 69 across the 8 of their papers we have counts for
5 papers · 1 filter
AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks
Sirui Li, Pietro Liò b, Xinsheng Li +2
Hypergraph neural networks have achieved significant success in recent years. However, manual architecture crafting is labor-intensive and often fails to capture complex, higher-or…
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang, Wencai Cao, Hoang Ta +2
Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are propose…
An end-to-end attention-based approach for learning on graphs
David Buterez, Jon Paul Janet, Dino Oglic +1
There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersed…
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
Mehedi Hasan, Moloud Abdar, Abbas Khosravi +5
Although neural networks (especially deep neural networks) have achieved \textit{better-than-human} performance in many fields, their real-world deployment is still questionable du…
A Federated Learning Benchmark for Drug-Target Interaction
Gianluca Mittone, Filip Svoboda, Marco Aldinucci +2
Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to reg…