3 citations · 3 across the 5 of their papers we have counts for
7 papers
Markovian Pre-Trained Transformer for Next-Item Recommendation
Cong Xu, Guoliang Li, Jun Wang +1
We introduce the Markovian Pre-trained Transformer (MPT) for next-item recommendation, a transferable model fully pre-trained on synthetic Markov chains, yet capable of achieving s…
Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Jun Wang
Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during…
Consistent Assistant Domains Transformer for Source-free Domain Adaptation
Renrong Shao, Wei Zhang, Kangyang Luo +2
Source-free domain adaptation (SFDA) aims to address the challenge of adapting to a target domain without accessing the source domain directly. However, due to the inaccessibility…
Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics
Cong Xu, Wenbin Liang, Mo Yu +7
The rapid scaling of models has led to prohibitively high training and fine-tuning costs. A major factor accounting for memory consumption is the widespread use of stateful optimiz…
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models
Runze Li, Siyu Wu, Jun Wang +1
Knowledge Tracing (KT) aims to model a student's learning state over time and predict their future performance. However, traditional KT methods often face challenges in explainabil…
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information
Yunhang He, Cong Xu, Jun Wang +1
Graph Neural Networks (GNNs) have demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data fo…