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Found 3 papers, 1 papers with code
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
In this work, we address said challenges by applying fidelity kernels to real-world data with unknown structure, related to the scheduling of a fleet of electric vehicles, and to synthetic data generated from the union of subspaces, which is then close to many relevant real-world datasets.
Information Flow in Graph Neural Networks: A Clinical Triage Use Case
Specifically, we propose a mathematical model that decouples the GNN connectivity from the connectivity of the graph data and evaluate the performance of GNNs in a clinical triage use case.
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences.
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