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Found 3 papers, 2 papers with code
Masked Attention is All You Need for Graphs
Graph neural networks (GNNs) and variations of the message passing algorithm are the predominant means for learning on graphs, largely due to their flexibility, speed, and satisfactory performance.
Graph Neural Networks with Adaptive Readouts
We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function.
Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning
Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing.
