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Found 2 papers, 1 papers with code
Residual Attention Net for Superior Cross-Domain Time Sequence Modeling
We present a novel architecture, residual attention net (RAN), which merges a sequence architecture, universal transformer, and a computer vision architecture, residual net, with a high-way architecture for cross-domain sequence modeling.
Graph Star Net for Generalized Multi-Task Learning
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
Ranked #1 on
Link Prediction
on Pubmed (biased evaluation)
