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By adversarially synthesizing graph structures and self-correcting node labels, AdvSynGNN achieves state-of-the-art robustness against structural noise and heterophily in graph neural networks.
Achieve robust multimodal sentiment analysis, even with noisy or incomplete data, by explicitly modeling hierarchical relationships in hyperbolic space.
Achieve a better trade-off between search precision and carbon footprint with GaiaFlow, a framework that uses semantic-guided diffusion tuning to make neural search systems more sustainable.
