activity
20212024
most citedSelf-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2024

Real-time Contact State Estimation in Shape Control of Deformable Linear Objects under Small Environmental Constraints

Kejia Chen, Zhenshan Bing, Yansong Wu +4

Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust…

cs.SI2023

Balancing Augmentation with Edge-Utility Filter for Signed GNNs

Ke-Jia Chen, Yaming Ji, Youran Qu +1

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The exi…

cs.RO2023

Contact-aware Shaping and Maintenance of Deformable Linear Objects With Fixtures

Kejia Chen, Zhenshan Bing, Fan Wu +4

Studying the manipulation of deformable linear objects has significant practical applications in industry, including car manufacturing, textile production, and electronics automati…

cs.RO2023

Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor

Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4

Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…

cs.LG20214 cited

Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast

Linpu Jiang, Ke-Jia Chen, Jingqiang Chen

Self-supervised learning on graphs has recently drawn a lot of attention due to its independence from labels and its robustness in representation. Current studies on this topic mai…

cs.IR2021

GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation

Jie Zhang, Ke-Jia Chen, Jingqiang Chen

Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional v…