activity
20172022
most citedFast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

4 citations · 8 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CL2022

Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer

Woojeong Jin, Dong-Ho Lee, Chenguang Zhu +2

Pre-trained language models are still far from human performance in tasks that need understanding of properties (e.g. appearance, measurable quantity) and affordances of everyday o…

cs.CV2021

MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding

Woojeong Jin, Maziar Sanjabi, Shaoliang Nie +3

To reduce a model size but retain performance, we often rely on knowledge distillation (KD) which transfers knowledge from a large "teacher" model to a smaller "student" model. How…

cs.LG2020

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Sankalp Garg, Navodita Sharma, Woojeong Jin +1

Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowle…

cs.AI2019

Collaborative Policy Learning for Open Knowledge Graph Reasoning

Cong Fu, Tong Chen, Meng Qu +2

In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse an…

cs.LG2019

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

Woojeong Jin, Meng Qu, Xisen Jin +1

Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a ti…

cs.IR20191 cited

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Weizhi Ma, Min Zhang, Yue Cao +6

Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation…