activity
20212023
most citedAutomated Prompting for Non-overlapping Cross-domain Sequential Recommendation

7 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR20237 cited

Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation

Lei Guo, Chunxiao Wang, Xinhua Wang +2

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.…

cs.RO2023

Sector Bounds for Vertical Cable Force Error in Cable-Suspended Load Transportation System

Lidan Xu, Hao Lu, JianLiang Wang +2

This article studies the collaborative transportation of a cable-suspended pipe by two quadrotors. A force-coordination control scheme is proposed, where a force-consensus term is…

cs.NI2023

Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks

Yingying Guan, Qingyang Song, Weijing Qi +3

The increasing demand for diverse emerging applications has resulted in the interconnection of multi-access edge computing (MEC) systems via metro optical networks. To cater to the…

cs.LG2022

Deep Forest with Hashing Screening and Window Screening

Pengfei Ma, Youxi Wu, Yan Li +4

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vecto…

cs.LG20224 cited

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Haoteng Tang, Guixiang Ma, Lei Guo +3

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional network…

cs.LG2021

DBC-Forest: Deep forest with binning confidence screening

Pengfei Ma, Youxi Wu, Yan Li +2

As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications. Compared with the traditional deep forest approach, gcFo…