8 citations · 10 across the 3 of their papers we have counts for
6 papers
Learning to Detect Few-Shot-Few-Clue Misinformation
Qiang Zhang, Hongbin Huang, Shangsong Liang +2
The quality of digital information on the web has been disquieting due to the lack of careful manual review. Consequently, a large volume of false textual information has been diss…
Addressing Class-Imbalance Problem in Personalized Ranking
Lu Yu, Shichao Pei, Chuxu Zhang +4
Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{…
Semi-supervisedly Co-embedding Attributed Networks
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang +1
Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a principled framework to effectively gen…
Variational Self-attention Model for Sentence Representation
Qiang Zhang, Shangsong Liang, Emine Yilmaz
This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. We model the self-attention vector as random variables by…
Neural Variational Hybrid Collaborative Filtering
Teng Xiao, Shangsong Liang, Hong Shen +1
Collaborative Filtering (CF) is one of the most used methods for Recommender System. Because of the Bayesian nature and nonlinearity, deep generative models, e.g. Variational Autoe…
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
Xisen Jin, Wenqiang Lei, Zhaochun Ren +4
The task of dialogue generation aims to automatically provide responses given previous utterances. Tracking dialogue states is an important ingredient in dialogue generation for es…