Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
Diffusion model (DM), as a powerful generative model, recently achieved ...
Generative models, especially diffusion models (DMs), have achieved prom...
We present a new model for generating molecular data by combining discre...
Homophily principle, i.e. nodes with the same labels are more likely to ...
Predicting molecular conformations (or 3D structures) from molecular gra...
We study a fundamental problem in computational chemistry known as molec...
We study how to generate molecule conformations (i.e., 3D
structures) fr...
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) dua...
Despite the recent success on image classification, self-training has on...
We tackle a common scenario in imitation learning (IL), where agents try...
Generative Adversarial Networks (GANs) have shown great promise in model...
Although Shannon theory states that it is asymptotically optimal to sepa...
A fundamental problem in computational chemistry is to find a set of
rea...
Molecular graph generation is a fundamental problem for drug discovery a...