619 citations · 3.7k across the 114 of their papers we have counts for
12 papers · 1 filter
Prototypical Graph Contrastive Learning
Shuai Lin, Pan Zhou, Zi-Yuan Hu +6
Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generall…
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Siyi Hu, Fengda Zhu, Xiaojun Chang +1
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model…
Iterative Graph Self-Distillation
Hanlin Zhang, Shuai Lin, Weiyang Liu +4
Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distilla…
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang +4
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains w…
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
Ziliang Chen, Jingyu Zhuang, Xiaodan Liang +1
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-in…
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Haowen Xu, Hao Zhang, Zhiting Hu +3
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the o…