210 citations · 727 across the 35 of their papers we have counts for
6 papers · 1 filter
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…
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
Yuxuan Song, Minkai Xu, Lantao Yu +3
Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios t…
Energy-Based Imitation Learning
Minghuan Liu, Tairan He, Minkai Xu +1
We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environmen…
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi, Minkai Xu, Hongyu Guo +2
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods…
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu +3
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating c…