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Eric Inae

2 accepted papers

2025

Learning Repetition-Invariant Representations for Polymer Informatics

NeurIPS 2025poster

Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aerospace. However, existing graph neural network methods, though effective for small molecules, only model the single unit…

Cited by 0SourceScholar
2023

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

NeurIPS 2023poster

Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then…