Introduces hGAO and cGAO operators for graph representation learning that outperform standard graph attention operators in accuracy while reducing computational requirements.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
MTDS with TokenMoE improves inform rate by 8.1% and success rate by 0.8% over single-module baselines on a benchmark dataset.
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Graph Representation Learning via Hard and Channel-Wise Attention Networks
Introduces hGAO and cGAO operators for graph representation learning that outperform standard graph attention operators in accuracy while reducing computational requirements.
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A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
MTDS with TokenMoE improves inform rate by 8.1% and success rate by 0.8% over single-module baselines on a benchmark dataset.