PACT introduces a peak-aware cross-attention graph transformer that emulates station-level storm surges more accurately than prior graph neural network baselines while running in seconds after training.
and Ying, Rex and Leskovec, Jure , title =
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Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
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PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation
PACT introduces a peak-aware cross-attention graph transformer that emulates station-level storm surges more accurately than prior graph neural network baselines while running in seconds after training.
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.