3 accepted papers
Recently, structure–text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks and language models. However, existing methods typically rely on homophily assumptions in similarity estimation and hard optimiza
Designing molecules with desired properties, aka the oRiented molEcule Design (RED), is a fundamental task in chemistry and materials science. While graph diffusion models (GDMs) and reinforcement learning techniques (RL) show promise in molecule structure generation and property optimization stages
While 3D Gaussian Splatting (3DGS) delivers high-quality, real-time rendering for bounded scenes, its extension to large-scale urban environments introduces critical challenges in geometric consistency, memory efficiency, and computational scalability. We present UrbanGS, a scalable reconstruction f…