27 citations · 42 across the 7 of their papers we have counts for
7 papers
Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning
Siyang Yuan, Pengyu Cheng, Ruiyi Zhang +3
Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made pro…
Reinforcement Learning for Flexibility Design Problems
Yehua Wei, Lei Zhang, Ruiyi Zhang +3
Flexibility design problems are a class of problems that appear in strategic decision-making across industries, where the objective is to design a (, manufacturing) network t…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Improving Adversarial Text Generation by Modeling the Distant Future
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words…
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…
Learning to Recommend from Sparse Data via Generative User Feedback
Wenlin Wang, Hongteng Xu, Ruiyi Zhang +3
Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a l…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…