2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CL2020★ 1 cited
Nested-Wasserstein Self-Imitation Learning for Sequence Generation
Ruiyi Zhang, Changyou Chen, Zhe Gan +3
Reinforcement learning (RL) has been widely studied for improving sequence-generation models. However, the conventional rewards used for RL training typically cannot capture suffic…
cs.CV2019★ 2 cited
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Zhenyi Wang, Ping Yu, Yang Zhao +4
Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence…