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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.
Make your multimodal models immune to missing modalities with a training framework that selectively collapses modality information, boosting robustness without sacrificing performance.
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.
