activity
20202022
most citedCauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

116 citations · 256 across the 11 of their papers we have counts for

collaborators

13 papers

cs.SD202217 cited

Contrastive Learning with Positive-Negative Frame Mask for Music Representation

Dong Yao, Zhou Zhao, Shengyu Zhang +4

Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in t…

eess.IV20223 cited

BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation

Wenqiao Zhang, Lei Zhu, James Hallinan +4

In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the po…

cs.CV20222 cited

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Mengze Li, Tianbao Wang, Haoyu Zhang +9

Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing…

cs.CV2022

A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation

Dongqi Wang, Shengyu Zhang, Zhipeng Di +3

Network pruning and knowledge distillation are two widely-known model compression methods that efficiently reduce computation cost and model size. A common problem in both pruning…

cs.IR20212 cited

Multi-trends Enhanced Dynamic Micro-video Recommendation

Yujie Lu, Yingxuan Huang, Shengyu Zhang +4

The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized micro-video discovery with ease. Recent advances in micro-vi…

cs.LG20211 cited

Stable Prediction on Graphs with Agnostic Distribution Shift

Shengyu Zhang, Kun Kuang, Jiezhong Qiu +5

Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with rand…