activity
20222026
most citedExploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

41 citations · 52 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement

Xichen Yan, Zelin Zang, Changxi Chi +8

A critical challenge in single-cell RNA sequencing (scRNA-seq) integration is resolving the tension between eliminating batch effects and maintaining biological fidelity. While rec…

cs.HC2023

Deepsea: A Meta-ocean Prototype for Undersea Exploration

Jinyu Li, Ping Hu, Weicheng Cui +2

Metaverse has attracted great attention from industry and academia in recent years. Metaverse for the ocean (Meta-ocean) is the implementation of the Metaverse technologies in virt…

cs.CY2023★ 5 cited

The current opportunities and challenges of Web 3.0

Yuqing Fan, Tianyi Huang, Yiran Meng +1

With recent advancements in AI and 5G technologies,as well as the nascent concepts of blockchain and metaverse,a new revolution of the Internet,known as Web 3.0,is emerging. Given…

cs.IR2023★ 41 cited

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

Junchen Fu, Fajie Yuan, Yu Song +6

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstr…

cs.AI2023★ 3 cited

High-dimensional Clustering onto Hamiltonian Cycle

Tianyi Huang, Shenghui Cheng, Stan Z. Li +1

Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering…

cs.CY2023

Roadmap towards Meta-being

Tianyi Huang, Stan Z. Li, Xin Yuan +1

Metaverse is a perpetual and persistent multi-user environment that merges physical reality with digital virtuality. It is widely considered to be the next revolution of the Intern…

cs.LG2022★ 3 cited

EVNet: An Explainable Deep Network for Dimension Reduction

Zelin Zang, Shenghui Cheng, Linyan Lu +7

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful propertie…