From the 1 of 44 linked papers with an AI index.
1 citations · 2 across the 7 of their papers we have counts for
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DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models
Liang Wang, Yu Rong, Tingyang Xu +7
Molecular structure elucidation from spectra is a fundamental challenge in molecular science. Conventional approaches rely heavily on expert interpretation and lack scalability, wh…
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Dingshuo Chen, Shuchen Xue, Liuji Chen +5
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progres…
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Liang Wang, Shaozhen Liu, Yu Rong +3
Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. Howe…
GraphFM: Graph Factorization Machines for Feature Interaction Modeling
Shu Wu, Zekun Li, Yunyue Su +3
Factorization machine (FM) is a prevalent approach to modeling pairwise (second-order) feature interactions when dealing with high-dimensional sparse data. However, on the one hand…
Diffusion Models for Molecules: A Survey of Methods and Tasks
Liang Wang, Chao Song, Zhiyuan Liu +3
Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant…
Bi-Level Graph Structure Learning for Next POI Recommendation
Liang Wang, Shu Wu, Qiang Liu +3
Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs)…