14 citations · 16 across the 5 of their papers we have counts for
5 papers
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Jana Vatter, Mykhaylo Zayats, Marcos Martínez Galindo +4
With the ever-growing size of real-world graphs, numerous techniques to overcome resource limitations when training Graph Neural Networks (GNNs) have been developed. One such appro…
Description Boosting for Zero-Shot Entity and Relation Classification
Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo +3
Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks t…
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Hoang Thanh Lam, Marco Luca Sbodio, Marcos Martínez Galindo +6
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases o…
Dynamic resource allocation for efficient parallel CFD simulations
G. Houzeaux, R. M. Badia, R. Borrell +4
CFD users of supercomputers usually resort to rule-of-thumb methods to select the number of subdomains (partitions) when relying on MPI-based parallelization. One common approach i…
Ensembling Graph Predictions for AMR Parsing
Hoang Thanh Lam, Gabriele Picco, Yufang Hou +5
In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into depe…