A geometry-adaptive Riemannian framework for molecular representation learning, which explicitly models motifs as the basic units and learns their embeddings across multiple constant-curvature spaces, offering a general and scalable framework for scientific molecular modeling.
Fei Liu, Wen-Kai Lu, Fei-Long Wang et al.· Proceedings of the 32nd ACM...· 0 citations
A novel self-supervised framework for relational reasoning over RDBs that treats context sparsity as a controllable curriculum variable and leverages it to induce a progressive shift from semantic-dominant inference to structure-aware relational reasoning.
Yujie Tian, Kun Zhang, Qiu-Yuan Li et al.· Proceedings of the 32nd ACM...· 0 citations
Molecular properties are often governed by a small number of local substructures, or motifs, whose topologies can vary drastically across molecules. Existing molecular representation learning approaches typically embed all motifs into a single Euclidean or fixed-curvature space, which fails to capture the motif-level t...
Fei Liu, Wen-Kai Lu, Feilong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Relational databases (RDBs) play an essential role in real-world scenarios, such as e-commerce, social media, and industry. Recently, with the rapid development of Large Language Models (LLMs), using LLMs to exploit RDBs has become a significant trend. Plenty of works have been proposed to use natural language to descr...
Yujie Tian, Kun Zhang, Qiuyuan Li et al.· Proceedings of the 32nd ACM...· 0 citations
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