210 citations · 475 across the 8 of their papers we have counts for
9 papers
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
Minkai Xu, Lantao Yu, Yang Song +3
Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machi…
Learning Gradient Fields for Molecular Conformation Generation
Chence Shi, Shitong Luo, Minkai Xu +1
We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs. Existing mach…
Learning Neural Generative Dynamics for Molecular Conformation Generation
Minkai Xu, Shitong Luo, Yoshua Bengio +2
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationa…
Towards Generalized Implementation of Wasserstein Distance in GANs
Minkai Xu, Zhiming Zhou, Guansong Lu +3
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it doe…
Reciprocal Supervised Learning Improves Neural Machine Translation
Minkai Xu, Mingxuan Wang, Zhouhan Lin +3
Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT). This is m…
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Yuxuan Song, Qiwei Ye, Minkai Xu +1
Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \texti…