185 citations · 617 across the 26 of their papers we have counts for
26 papers
JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu
Hao Liu, Qian Gao, Jiang Li +9
In modern internet industries, deep learning based recommender systems have became an indispensable building block for a wide spectrum of applications, such as search engine, news…
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…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
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…