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DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion.

, , , , , and . ICLR, OpenReview.net, (2023)

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Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation., , , and . KDD, page 824-834. ACM, (2022)Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach., , and . NeurIPS, page 19435-19447. (2021)Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11329-11339. PMLR, (2021)Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions., , , , , , , and . WWW, page 3173-3184. ACM, (2024)Graph Out-of-Distribution Generalization via Causal Intervention., , , , and . WWW, page 850-860. ACM, (2024)Advective Diffusion Transformers for Topological Generalization in Graph Learning., , , , , and . CoRR, (2023)SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity., , , , and . CoRR, (2024)Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach., , , , , and . IJCAI, page 2355-2361. ijcai.org, (2022)MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning., , , and . WWW, page 4075-4085. ACM, (2023)How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space., , , , and . CoRR, (2023)